From 7dc12585af9c2586aeac230caa9a4f1172a15432 Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 30 Jul 2026 15:08:58 -0600 Subject: [PATCH 01/27] Added VLM Datasets and corresponding filters. Updated viewable labels to Titlecase using a labelOverride file except for the Crop Type filter due to excess crops. --- .gitignore | 2 +- src/components/DatasetMetadataModal.tsx | 62 ++- src/css/custom.css | 360 ++++++------- src/lib/datasets.ts | 78 ++- src/lib/labelOverrides.ts | 71 +++ src/pages/datasets/index.module.css | 5 + src/pages/datasets/index.tsx | 53 +- static/data/datasets.json | 4 +- static/data/hf_datasets.json | 639 ++++++++++++++++++++---- 9 files changed, 960 insertions(+), 314 deletions(-) create mode 100644 src/lib/labelOverrides.ts diff --git a/.gitignore b/.gitignore index d322cf5..a0e3065 100644 --- a/.gitignore +++ b/.gitignore @@ -18,5 +18,5 @@ npm-debug.log* yarn-debug.log* yarn-error.log* - +yarn.lock .external \ No newline at end of file diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index e7bebe8..f6d343e 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -1,6 +1,6 @@ import { Fragment, useEffect, useMemo, useState } from 'react'; import type { Dataset } from '../lib/datasets'; -import { formatDisplayLocation } from '../lib/datasets'; +import { formatDisplayLocation, toTitleCase } from '../lib/datasets'; import { METRIC_CATEGORY_LABELS, useDatasetPerformance } from '../lib/performance'; import type { MetricCategory, PerformanceEntry } from '../lib/performance'; import styles from './DatasetMetadataModal.module.css'; @@ -17,8 +17,8 @@ function formatImageCount(count: number | null) { function formatValue(value: string | string[] | null | undefined) { if (value == null) return 'Unknown'; - if (Array.isArray(value)) return value.length ? value.join(', ') : 'Unknown'; - return value; + if (Array.isArray(value)) return value.length ? value.map(toTitleCase).join(', ') : 'Unknown'; + return toTitleCase(value); } function formatArray(value: number[] | null) { @@ -92,6 +92,13 @@ function formatMetricScore(entry: { metrics: { key: string; label: string; value function formatLoaderInstructions(dataset: Dataset) { if (dataset.source === 'huggingface') { + if(dataset.dataset_type === 'vlm') { + return { + title: 'Load from Hugging Face', + code: `from datasets import load_dataset\nloader = load_dataset("Project-AgML/${dataset.name}")`, + }; + } + return { title: 'Load from Hugging Face', code: `from agml.data.hf_loader import HuggingFaceDataLoader\nloader = HuggingFaceDataLoader("Project-AgML/${dataset.name}")`, @@ -248,11 +255,18 @@ export function DatasetMetadataModal({ if (!open || dataset == null) return null; + const isVlm = dataset.dataset_type === 'vlm'; + const metadataRows = [ ['Location', formatDisplayLocation(dataset.location)], - ['Sensor modality', formatValue(dataset.sensor_modality)], + ['Sensor modality', dataset.sensor_modality == null || dataset.sensor_modality === '' ? 'Unknown' : dataset.sensor_modality], ['Platform', formatValue(dataset.platform)], - ['Number of images', formatImageCount(dataset.num_images)], + ...(isVlm + ? ([ + ['Conversation format', formatValue(dataset.conversation_format)], + ['Num of samples', formatImageCount(dataset.num_rows)], + ] as const) + : ([['Number of images', formatImageCount(dataset.num_images)]] as const)), ['Size', formatBytesDecimal(dataset.zip_size_bytes)], ...(dataset.augmented_num_images != null ? ([['Augmented images', formatImageCount(dataset.augmented_num_images)]] as const) @@ -303,6 +317,39 @@ export function DatasetMetadataModal({ ))} + {isVlm && dataset.qa_type && dataset.qa_type.length > 0 && ( +
+

QA Types

+
+ {dataset.qa_type.map((value) => ( + {toTitleCase(value)} + ))} +
+
+ )} + + {isVlm && dataset.task_dimensions && dataset.task_dimensions.length > 0 && ( +
+

Task Dimensions

+
+ {dataset.task_dimensions.map((value) => ( + {toTitleCase(value)} + ))} +
+
+ )} + + {isVlm && dataset.source_datasets && dataset.source_datasets.length > 0 && ( +
+

Source Datasets

+
+ {dataset.source_datasets.map((sourceDataset) => ( + {sourceDataset} + ))} +
+
+ )} + {cropList.length > 0 && (

Crops

@@ -388,6 +435,11 @@ export function DatasetMetadataModal({ View on Hugging Face )} + {isVlm && dataset.parent_dataset && ( + + View Raw Dataset + + )} )} diff --git a/src/css/custom.css b/src/css/custom.css index 62cf4a0..f9993b5 100644 --- a/src/css/custom.css +++ b/src/css/custom.css @@ -1,4 +1,4 @@ -@import url('https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;500;600;700&family=Inter:wght@400;500;600;700&display=swap'); +@import url("https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;500;600;700&family=Inter:wght@400;500;600;700&display=swap"); /** * Any CSS included here will be global. The classic template @@ -8,104 +8,118 @@ /* You can override the default Infima variables here. */ :root { - --ifm-color-primary: oklch(0.5 0.14 150); - --ifm-color-primary-dark: oklch(0.45 0.14 150); - --ifm-color-primary-darker: oklch(0.42 0.14 150); - --ifm-color-primary-darkest: oklch(0.35 0.13 150); - --ifm-color-primary-light: oklch(0.55 0.14 150); - --ifm-color-primary-lighter: oklch(0.6 0.14 150); - --ifm-color-primary-lightest: oklch(0.7 0.13 150); - --ifm-font-family-base: 'Inter', 'Noto Sans', sans-serif; - --ifm-heading-font-family: 'Inter', 'Noto Sans', sans-serif; - --ifm-code-font-family: 'IBM Plex Mono', 'Fira Code', monospace; - --ifm-code-font-size: 95%; - --docusaurus-highlighted-code-line-bg: oklch(0.16 0.01 145 / 0.08); - --agml-page-bg: oklch(0.985 0.006 145); - --agml-surface: oklch(0.97 0.01 145); - --agml-surface-strong: oklch(1 0.002 145); - --agml-surface-soft: oklch(0.965 0.008 145); - --agml-border: oklch(0.85 0.012 145); - --agml-border-strong: oklch(0.78 0.014 145); - --agml-shadow: 0 16px 32px oklch(0 0 0 / 0.06); - --agml-shadow-strong: 0 24px 48px oklch(0 0 0 / 0.09); - --agml-text: oklch(0.2 0.012 145); - --agml-muted: oklch(0.45 0.012 145); - --agml-accent-soft: oklch(0.92 0.06 150); - --agml-accent-border: color-mix(in oklch, var(--ifm-color-primary) 35%, var(--agml-border)); - --agml-warning-soft: rgba(189, 139, 76, 0.12); - --agml-warning-text: #7a4e2a; - --agml-modal-overlay: oklch(0 0 0 / 0.3); - --agml-badge-classification-bg: oklch(0.89 0.10 145); - --agml-badge-classification-fg: oklch(0.34 0.13 145); - --agml-badge-detection-bg: oklch(0.89 0.08 175); - --agml-badge-detection-fg: oklch(0.36 0.12 175); - --agml-badge-segmentation-bg: oklch(0.89 0.07 110); - --agml-badge-segmentation-fg: oklch(0.36 0.11 110); - --agml-badge-other-bg: oklch(0.91 0.01 145); - --agml-badge-other-fg: oklch(0.4 0.01 145); - --agml-tag-bg: oklch(0.91 0.02 145); - --agml-tag-fg: oklch(0.36 0.03 145); - --ifm-background-color: transparent; - --ifm-navbar-background-color: var(--agml-surface); - --ifm-footer-background-color: var(--agml-surface); - --ifm-card-background-color: var(--agml-surface-strong); - --ifm-toc-background-color: var(--agml-surface); - --ifm-toc-border-color: var(--agml-border); + --ifm-color-primary: oklch(0.5 0.14 150); + --ifm-color-primary-dark: oklch(0.45 0.14 150); + --ifm-color-primary-darker: oklch(0.42 0.14 150); + --ifm-color-primary-darkest: oklch(0.35 0.13 150); + --ifm-color-primary-light: oklch(0.55 0.14 150); + --ifm-color-primary-lighter: oklch(0.6 0.14 150); + --ifm-color-primary-lightest: oklch(0.7 0.13 150); + --ifm-font-family-base: "Inter", "Noto Sans", sans-serif; + --ifm-heading-font-family: "Inter", "Noto Sans", sans-serif; + --ifm-code-font-family: "IBM Plex Mono", "Fira Code", monospace; + --ifm-code-font-size: 95%; + --docusaurus-highlighted-code-line-bg: oklch(0.16 0.01 145 / 0.08); + --agml-page-bg: oklch(0.985 0.006 145); + --agml-surface: oklch(0.97 0.01 145); + --agml-surface-strong: oklch(1 0.002 145); + --agml-surface-soft: oklch(0.965 0.008 145); + --agml-border: oklch(0.85 0.012 145); + --agml-border-strong: oklch(0.78 0.014 145); + --agml-shadow: 0 16px 32px oklch(0 0 0 / 0.06); + --agml-shadow-strong: 0 24px 48px oklch(0 0 0 / 0.09); + --agml-text: oklch(0.2 0.012 145); + --agml-muted: oklch(0.45 0.012 145); + --agml-accent-soft: oklch(0.92 0.06 150); + --agml-accent-border: color-mix( + in oklch, + var(--ifm-color-primary) 35%, + var(--agml-border) + ); + --agml-warning-soft: rgba(189, 139, 76, 0.12); + --agml-warning-text: #7a4e2a; + --agml-modal-overlay: oklch(0 0 0 / 0.3); + --agml-badge-classification-bg: oklch(0.89 0.1 145); + --agml-badge-classification-fg: oklch(0.34 0.13 145); + --agml-badge-detection-bg: oklch(0.89 0.08 175); + --agml-badge-detection-fg: oklch(0.36 0.12 175); + --agml-badge-segmentation-bg: oklch(0.89 0.07 110); + --agml-badge-segmentation-fg: oklch(0.36 0.11 110); + --agml-badge-other-bg: oklch(0.91 0.01 145); + --agml-badge-other-fg: oklch(0.4 0.01 145); + --agml-badge-vlm-bg: oklch(0.89 0.08 300); + --agml-badge-vlm-fg: oklch(0.36 0.13 300); + --agml-tag-bg: oklch(0.91 0.02 145); + --agml-tag-fg: oklch(0.36 0.03 145); + --ifm-background-color: transparent; + --ifm-navbar-background-color: var(--agml-surface); + --ifm-footer-background-color: var(--agml-surface); + --ifm-card-background-color: var(--agml-surface-strong); + --ifm-toc-background-color: var(--agml-surface); + --ifm-toc-border-color: var(--agml-border); } /* For readability concerns, you should choose a lighter palette in dark mode. */ -html[data-theme='dark'] { - --ifm-color-primary: oklch(0.72 0.15 150); - --ifm-color-primary-dark: oklch(0.65 0.15 150); - --ifm-color-primary-darker: oklch(0.6 0.15 150); - --ifm-color-primary-darkest: oklch(0.5 0.14 150); - --ifm-color-primary-light: oklch(0.78 0.14 150); - --ifm-color-primary-lighter: oklch(0.83 0.13 150); - --ifm-color-primary-lightest: oklch(0.88 0.11 150); - --docusaurus-highlighted-code-line-bg: oklch(0 0 0 / 0.35); - --agml-page-bg: oklch(0.16 0.01 145); - --agml-surface: oklch(0.18 0.012 145); - --agml-surface-strong: oklch(0.19 0.012 145); - --agml-surface-soft: oklch(0.17 0.01 145); - --agml-border: oklch(0.28 0.014 145); - --agml-border-strong: oklch(0.3 0.014 145); - --agml-shadow: 0 16px 32px oklch(0 0 0 / 0.28); - --agml-shadow-strong: 0 24px 48px oklch(0 0 0 / 0.34); - --agml-text: oklch(0.92 0.006 145); - --agml-muted: oklch(0.65 0.01 145); - --agml-accent-soft: oklch(0.22 0.05 150); - --agml-accent-border: color-mix(in oklch, var(--ifm-color-primary) 30%, var(--agml-border)); - --agml-warning-soft: rgba(189, 139, 76, 0.18); - --agml-warning-text: #d9b084; - --agml-modal-overlay: oklch(0 0 0 / 0.62); - --agml-badge-classification-bg: oklch(0.30 0.10 145); - --agml-badge-classification-fg: oklch(0.88 0.15 145); - --agml-badge-detection-bg: oklch(0.30 0.09 175); - --agml-badge-detection-fg: oklch(0.86 0.13 175); - --agml-badge-segmentation-bg: oklch(0.30 0.07 110); - --agml-badge-segmentation-fg: oklch(0.86 0.12 110); - --agml-badge-other-bg: oklch(0.30 0.02 145); - --agml-badge-other-fg: oklch(0.8 0.02 145); - --agml-tag-bg: oklch(0.24 0.014 145); - --agml-tag-fg: oklch(0.75 0.012 145); - --ifm-background-color: transparent; - --ifm-navbar-background-color: var(--agml-surface); - --ifm-footer-background-color: var(--agml-surface); - --ifm-card-background-color: var(--agml-surface-strong); - --ifm-toc-background-color: var(--agml-surface); - --ifm-toc-border-color: var(--agml-border); +html[data-theme="dark"] { + --ifm-color-primary: oklch(0.72 0.15 150); + --ifm-color-primary-dark: oklch(0.65 0.15 150); + --ifm-color-primary-darker: oklch(0.6 0.15 150); + --ifm-color-primary-darkest: oklch(0.5 0.14 150); + --ifm-color-primary-light: oklch(0.78 0.14 150); + --ifm-color-primary-lighter: oklch(0.83 0.13 150); + --ifm-color-primary-lightest: oklch(0.88 0.11 150); + --docusaurus-highlighted-code-line-bg: oklch(0 0 0 / 0.35); + --agml-page-bg: oklch(0.16 0.01 145); + --agml-surface: oklch(0.18 0.012 145); + --agml-surface-strong: oklch(0.19 0.012 145); + --agml-surface-soft: oklch(0.17 0.01 145); + --agml-border: oklch(0.28 0.014 145); + --agml-border-strong: oklch(0.3 0.014 145); + --agml-shadow: 0 16px 32px oklch(0 0 0 / 0.28); + --agml-shadow-strong: 0 24px 48px oklch(0 0 0 / 0.34); + --agml-text: oklch(0.92 0.006 145); + --agml-muted: oklch(0.65 0.01 145); + --agml-accent-soft: oklch(0.22 0.05 150); + --agml-accent-border: color-mix( + in oklch, + var(--ifm-color-primary) 30%, + var(--agml-border) + ); + --agml-warning-soft: rgba(189, 139, 76, 0.18); + --agml-warning-text: #d9b084; + --agml-modal-overlay: oklch(0 0 0 / 0.62); + --agml-badge-classification-bg: oklch(0.3 0.1 145); + --agml-badge-classification-fg: oklch(0.88 0.15 145); + --agml-badge-detection-bg: oklch(0.3 0.09 175); + --agml-badge-detection-fg: oklch(0.86 0.13 175); + --agml-badge-segmentation-bg: oklch(0.3 0.07 110); + --agml-badge-segmentation-fg: oklch(0.86 0.12 110); + --agml-badge-other-bg: oklch(0.3 0.02 145); + --agml-badge-other-fg: oklch(0.8 0.02 145); + --agml-badge-vlm-bg: oklch(0.3 0.09 300); + --agml-badge-vlm-fg: oklch(0.86 0.13 300); + --agml-tag-bg: oklch(0.24 0.014 145); + --agml-tag-fg: oklch(0.75 0.012 145); + --ifm-background-color: transparent; + --ifm-navbar-background-color: var(--agml-surface); + --ifm-footer-background-color: var(--agml-surface); + --ifm-card-background-color: var(--agml-surface-strong); + --ifm-toc-background-color: var(--agml-surface); + --ifm-toc-border-color: var(--agml-border); } html, body, #__docusaurus { - background: var(--agml-page-bg); - color: var(--agml-text); - overflow-x: hidden; + background: var(--agml-page-bg); + color: var(--agml-text); + overflow-x: hidden; } body { - transition: background-color 0.2s ease, color 0.2s ease; + transition: + background-color 0.2s ease, + color 0.2s ease; } .navbar, @@ -120,7 +134,7 @@ body { .theme-admonition, .theme-code-block, .table-of-contents__link { - color: var(--agml-text); + color: var(--agml-text); } .navbar, @@ -133,172 +147,172 @@ body { .card, .theme-admonition, .theme-code-block { - background: var(--agml-surface); - border-color: var(--agml-border); - box-shadow: var(--agml-shadow); + background: var(--agml-surface); + border-color: var(--agml-border); + box-shadow: var(--agml-shadow); } .navbar, .footer { - backdrop-filter: blur(14px); + backdrop-filter: blur(14px); } .navbar { - border-bottom: 1px solid var(--agml-border); - --ifm-navbar-height: 64px; - --ifm-navbar-padding-horizontal: 24px; - --ifm-navbar-item-padding-horizontal: 0; - --ifm-navbar-item-padding-vertical: 0; + border-bottom: 1px solid var(--agml-border); + --ifm-navbar-height: 64px; + --ifm-navbar-padding-horizontal: 24px; + --ifm-navbar-item-padding-horizontal: 0; + --ifm-navbar-item-padding-vertical: 0; } .navbar__brand { - margin-right: 20px; + margin-right: 20px; } .navbar__brand::before { - content: ''; - display: inline-block; - width: 10px; - height: 10px; - margin-right: 10px; - border-radius: 2px; - background: var(--ifm-color-primary); - flex-shrink: 0; + content: ""; + display: inline-block; + width: 10px; + height: 10px; + margin-right: 10px; + border-radius: 2px; + background: var(--ifm-color-primary); + flex-shrink: 0; } .navbar__title { - font-family: var(--ifm-code-font-family); - font-weight: 600; - font-size: 15px; - letter-spacing: -0.2px; + font-family: var(--ifm-code-font-family); + font-weight: 600; + font-size: 15px; + letter-spacing: -0.2px; } .navbar__item { - padding: 4px; + padding: 4px; } .navbar__link { - font-family: var(--ifm-code-font-family); - font-size: 12.5px; - font-weight: 400; - padding: 8px 12px; - border-radius: 6px; - color: var(--agml-muted); + font-family: var(--ifm-code-font-family); + font-size: 12.5px; + font-weight: 400; + padding: 8px 12px; + border-radius: 6px; + color: var(--agml-muted); } .navbar__link:hover, .navbar__link--active { - background: var(--agml-tag-bg); - color: var(--agml-text); + background: var(--agml-tag-bg); + color: var(--agml-text); } .navbar__github-button .navbar__link { - color: var(--ifm-navbar-background-color); - background: var(--ifm-color-primary); - font-weight: 600; + color: var(--ifm-navbar-background-color); + background: var(--ifm-color-primary); + font-weight: 600; } .navbar__github-button .navbar__link:hover { - background: var(--ifm-color-primary-dark); - color: var(--ifm-navbar-background-color); + background: var(--ifm-color-primary-dark); + color: var(--ifm-navbar-background-color); } .theme-doc-markdown { - border-radius: 0; - padding: 0; - background: transparent; - border: none; - box-shadow: none; - max-width: 760px; + border-radius: 0; + padding: 0; + background: transparent; + border: none; + box-shadow: none; + max-width: 760px; } .theme-doc-markdown.markdown h1 { - font-size: 1.875rem; - font-weight: 600; - letter-spacing: -0.01em; - margin-bottom: 0.5rem; + font-size: 1.875rem; + font-weight: 600; + letter-spacing: -0.01em; + margin-bottom: 0.5rem; } .theme-doc-markdown.markdown h2 { - font-size: 1.1875rem; - font-weight: 600; - scroll-margin-top: 1.25rem; + font-size: 1.1875rem; + font-weight: 600; + scroll-margin-top: 1.25rem; } .theme-doc-markdown.markdown h3 { - font-size: 1rem; - font-weight: 600; - scroll-margin-top: 1.25rem; + font-size: 1rem; + font-weight: 600; + scroll-margin-top: 1.25rem; } .theme-doc-markdown.markdown p, .theme-doc-markdown.markdown li { - font-size: 0.906rem; - line-height: 1.65; + font-size: 0.906rem; + line-height: 1.65; } .main-wrapper { - padding-top: 1.5rem; - padding-bottom: 2rem; + padding-top: 1.5rem; + padding-bottom: 2rem; } .theme-doc-sidebar-container { - border-right: 1px solid var(--agml-border); - background: var(--agml-surface-soft); - box-shadow: none; + border-right: 1px solid var(--agml-border); + background: var(--agml-surface-soft); + box-shadow: none; } .theme-doc-sidebar-menu { - --ifm-menu-color: var(--agml-muted); - --ifm-menu-color-active: var(--agml-text); - --ifm-menu-color-background-hover: var(--agml-tag-bg); - --ifm-menu-color-background-active: var(--agml-tag-bg); - --ifm-menu-link-padding-vertical: 0.5rem; - --ifm-menu-link-padding-horizontal: 0.5rem; - padding: 1.5rem 1rem; + --ifm-menu-color: var(--agml-muted); + --ifm-menu-color-active: var(--agml-text); + --ifm-menu-color-background-hover: var(--agml-tag-bg); + --ifm-menu-color-background-active: var(--agml-tag-bg); + --ifm-menu-link-padding-vertical: 0.5rem; + --ifm-menu-link-padding-horizontal: 0.5rem; + padding: 1.5rem 1rem; } .menu__link { - font-size: 0.82rem; - font-weight: 400; + font-size: 0.82rem; + font-weight: 400; } .sidebarCategoryLabel > .menu__list-item-collapsible { - background: transparent !important; + background: transparent !important; } .sidebarCategoryLabel > .menu__list-item-collapsible > .menu__link { - font-family: var(--ifm-code-font-family); - font-size: 0.68rem; - font-weight: 600; - letter-spacing: 0.06em; - text-transform: uppercase; - color: var(--agml-muted); - padding: 0.4rem 0.5rem; - pointer-events: none; + font-family: var(--ifm-code-font-family); + font-size: 0.68rem; + font-weight: 600; + letter-spacing: 0.06em; + text-transform: uppercase; + color: var(--agml-muted); + padding: 0.4rem 0.5rem; + pointer-events: none; } .sidebarCategoryLabel + .sidebarCategoryLabel { - margin-top: 1.25rem; + margin-top: 1.25rem; } .theme-doc-sidebar-item-link, .table-of-contents__link, .footer__link-item { - color: var(--agml-text); + color: var(--agml-text); } .theme-doc-sidebar-item-link--active { - color: var(--ifm-color-primary); + color: var(--ifm-color-primary); } .theme-code-block { - border: 1px solid var(--agml-border); - border-radius: 8px; - box-shadow: none; + border: 1px solid var(--agml-border); + border-radius: 8px; + box-shadow: none; } .theme-code-block pre, .prism-code { - background: var(--agml-surface-strong) !important; + background: var(--agml-surface-strong) !important; } diff --git a/src/lib/datasets.ts b/src/lib/datasets.ts index 7ef07e5..10f298f 100644 --- a/src/lib/datasets.ts +++ b/src/lib/datasets.ts @@ -1,5 +1,6 @@ import { useEffect, useMemo, useState } from 'react'; import useBaseUrl from '@docusaurus/useBaseUrl'; +import { toDisplayLabel } from './labelOverrides'; export interface Dataset { name: string; @@ -12,7 +13,7 @@ export interface Dataset { crop_types: string[] | null; sensor_modality: string | null; real_or_synthetic: string | null; - platform: string | null; + platform: string[] | null; input_data_format: string | null; annotation_format: string | null; num_images: number | null; @@ -28,6 +29,14 @@ export interface Dataset { parent_dataset?: string | null; zip_size_bytes?: number | null; hf_link?: string | null; + // VLM (vision-language) dataset fields — absent/null on plain vision datasets. + dataset_type: 'vision' | 'vlm'; + qa_type: string[] | null; + task_dimensions: string[] | null; + num_task_types: number | null; + conversation_format: string | null; + num_rows: number | null; + source_datasets: string[] | null; } type DatasetRecord = Record; @@ -115,18 +124,25 @@ function normalizeDataset(raw: unknown): Dataset | null { const augmentedCounterpart = firstString(raw.augmented_counterpart) ?? (augmentedNumImages != null ? 'yes' : 'no'); + const mlTask = firstString(raw.machine_learning_task, raw.ml_task, raw.task); + const qaType = toStringArray(raw.qa_type); + const taskDimensions = toStringArray(raw.task_dimensions); + const isVlm = mlTask === 'image-text-to-text' || Boolean(qaType?.length) || Boolean(taskDimensions?.length); + return { name, - machine_learning_task: firstString(raw.machine_learning_task, raw.ml_task, raw.task), + machine_learning_task: mlTask, source: firstString(raw.source), agricultural_task: firstString(raw.agricultural_task, raw.ag_task), location: normalizeLocation(raw.location), environment: firstString(raw.environment, raw.env, raw.image_environment)?.toLowerCase() ?? null, augmented_counterpart: augmentedCounterpart, - crop_types: toStringArray(raw.crop_types ?? raw.cropType ?? raw.crop_type)?.map((c) => c.toLowerCase()) ?? null, - sensor_modality: firstString(raw.sensor_modality, raw.sensor, raw.modality), + crop_types: toStringArray(raw.crop_types ?? raw.cropType ?? raw.crop_type) + ?.map((c) => c.trim()) + .filter((c) => c && !/^\+.*\b(more|others?)\b/i.test(c)) ?? null, + sensor_modality: firstString(raw.sensor_modality, raw.sensor, raw.modality) ?? toStringArray(raw.sensor_modality)?.join(', ') ?? null, real_or_synthetic: firstString(raw.real_or_synthetic, raw.real_synthetic), - platform: firstString(raw.platform), + platform: parsePlatform(raw.platform), input_data_format: firstString(raw.input_data_format, raw.input_format), annotation_format: firstString(raw.annotation_format), num_images: toNumber(raw.num_images ?? raw.n_images ?? raw.image_count), @@ -142,6 +158,13 @@ function normalizeDataset(raw: unknown): Dataset | null { parent_dataset: firstString(raw.parent_dataset, raw.parentDataset), zip_size_bytes: toNumber(raw.zip_size_bytes ?? raw.zipSizeBytes), hf_link: firstString(raw.hf_link, raw.huggingface_link, raw.hf_url), + dataset_type: isVlm ? 'vlm' : 'vision', + qa_type: qaType, + task_dimensions: taskDimensions, + num_task_types: toNumber(raw.num_task_types), + conversation_format: firstString(raw.conversation_format), + num_rows: toNumber(raw.num_rows), + source_datasets: toStringArray((raw.composition as Record | undefined)?.source_datasets), }; } @@ -157,7 +180,9 @@ function mergeDataset(current: Dataset, incoming: Dataset): Dataset { crop_types: current.crop_types ?? incoming.crop_types, sensor_modality: current.sensor_modality ?? incoming.sensor_modality, real_or_synthetic: current.real_or_synthetic ?? incoming.real_or_synthetic, - platform: current.platform ?? incoming.platform, + platform: (current.platform || incoming.platform) + ? Array.from(new Set([...(current.platform ?? []), ...(incoming.platform ?? [])])) + : null, input_data_format: current.input_data_format ?? incoming.input_data_format, annotation_format: current.annotation_format ?? incoming.annotation_format, num_images: current.num_images ?? incoming.num_images, @@ -173,6 +198,13 @@ function mergeDataset(current: Dataset, incoming: Dataset): Dataset { parent_dataset: current.parent_dataset ?? incoming.parent_dataset, zip_size_bytes: current.zip_size_bytes ?? incoming.zip_size_bytes, hf_link: current.hf_link ?? incoming.hf_link, + dataset_type: current.dataset_type === 'vlm' || incoming.dataset_type === 'vlm' ? 'vlm' : 'vision', + qa_type: current.qa_type ?? incoming.qa_type, + task_dimensions: current.task_dimensions ?? incoming.task_dimensions, + num_task_types: current.num_task_types ?? incoming.num_task_types, + conversation_format: current.conversation_format ?? incoming.conversation_format, + num_rows: current.num_rows ?? incoming.num_rows, + source_datasets: current.source_datasets ?? incoming.source_datasets, }; } @@ -289,7 +321,7 @@ export function filterDatasets( d.machine_learning_task?.toLowerCase().includes(q) || d.environment?.toLowerCase().includes(q) || d.augmented_counterpart?.toLowerCase().includes(q) || - d.platform?.toLowerCase().includes(q) || + d.platform?.some((p) => p.toLowerCase().includes(q)) || locationMatches || cropMatches ); @@ -308,7 +340,7 @@ export function filterDatasets( out = out.filter((d) => d.agricultural_task != null && opts.agTasks!.includes(d.agricultural_task)); } if (opts.platforms?.length) { - out = out.filter((d) => d.platform != null && opts.platforms!.includes(d.platform)); + out = out.filter((d) => d.platform != null && d.platform.some((p) => opts.platforms!.includes(p))); } if (opts.realOrSynthetic?.length) { out = out.filter((d) => d.real_or_synthetic != null && opts.realOrSynthetic!.includes(d.real_or_synthetic)); @@ -334,15 +366,39 @@ export function useDatasetOptions(data: Dataset[]) { augmentedCounterparts: unique(data.map((d) => d.augmented_counterpart)), cropTypes: unique(data.flatMap((d) => d.crop_types ?? [])), locations: unique(data.map((d) => d.location)), - platforms: unique(data.map((d) => d.platform)), + platforms: unique(data.flatMap((d) => d.platform ?? [])), realOptions: unique(data.map((d) => d.real_or_synthetic)), + datasetTypes: unique(data.map((d) => d.dataset_type)), + qaTypes: unique(data.flatMap((d) => d.qa_type ?? [])), + taskDimensions: unique(data.flatMap((d) => d.task_dimensions ?? [])), }; }, [data]); } +export function toTitleCase(value: string): string { + return toDisplayLabel(value); +} + +function parsePlatform(raw: unknown): string[] | null { + const tokens: string[] = []; + const pushSplit = (s: string) => s.split(',').forEach((part) => { const t = part.trim(); if (t) tokens.push(t); }); + if (Array.isArray(raw)) { + raw.forEach((entry) => { if (typeof entry === 'string') pushSplit(entry); }); + } else if (typeof raw === 'string') { + pushSplit(raw); + } + if (!tokens.length) return null; + const seen = new Map(); + for (const t of tokens) { + const key = t.toLowerCase(); + if (!seen.has(key)) seen.set(key, toTitleCase(t)); + } + return Array.from(seen.values()); +} + export function formatDisplayLocation(value: string | string[] | null | undefined): string { if (value == null) return '—'; - return Array.isArray(value) ? value.join(', ') : value; + return Array.isArray(value) ? value.map(toTitleCase).join(', ') : toTitleCase(value); } export interface DatasetStats { @@ -367,4 +423,4 @@ export function computeDatasetStats(datasets: Dataset[]): DatasetStats { imageCount, taskTypeCount, }; -} +} \ No newline at end of file diff --git a/src/lib/labelOverrides.ts b/src/lib/labelOverrides.ts new file mode 100644 index 0000000..b29596f --- /dev/null +++ b/src/lib/labelOverrides.ts @@ -0,0 +1,71 @@ +// Explicit display-label overrides for raw dataset field values (task types, platforms, +// modalities, QA types, etc). Add an entry here whenever the auto-generated Title Case +// (underscores/hyphens replaced, each word capitalized) isn't right — acronyms, special casing. +export const LABEL_OVERRIDES: Record = { + /* machine_learning_task */ + "image-text-to-text": "Image Text To Text", + image_classification: "Image Classification", + object_detection: "Object Detection", + semantic_segmentation: "Semantic Segmentation", + instance_segmentation: "Instance Segmentation", + image_regression: "Image Regression", + + /* qa_type */ + judgement: "Judgement", + counting: "Counting", + multiple_choice_text: "Multiple Choice Text", + multiple_choice_image: "Multiple Choice Image", + open_ended: "Open Ended", + long_form_qa: "Long Form QA", + + /* conversation_format */ + single_turn_reasoning: "Single Turn Reasoning", + multi_turn_reasoning: "Multi Turn Reasoning", + mixed: "Mixed", + + /* environment */ + field: "Field", + lab: "Lab", + + /* real_or_synthetic */ + real: "Real", + synthetic: "Synthetic", + + /* platform */ + ground: "Ground", + "ground camera": "Ground Camera", + handheld: "Handheld", + "handheld/ground": "Handheld/Ground", + ground_fixed: "Ground Fixed", + ground_mobile: "Ground Mobile", + aerial: "Aerial", + satellite: "Satellite", + uav: "UAV", + uas: "UAS", + + /* sensor_modality (acronym casing, used where title-cased elsewhere) */ + rgb: "RGB", + nir: "NIR", + "nir multispectral": "NIR Multispectral", + multispectral: "Multispectral", + gps: "GPS", + lidar: "LIDAR", + uv: "UV", + ir: "IR", +}; + +function autoTitleCase(value: string): string { + return value + .replace(/[_-]/g, " ") + .split(" ") + .map((word) => + word ? word[0].toUpperCase() + word.slice(1).toLowerCase() : word, + ) + .join(" "); +} + +// Looks up an explicit override (case-insensitive); falls back to auto Title Case. +export function toDisplayLabel(value: string): string { + const override = LABEL_OVERRIDES[value.toLowerCase()]; + return override ?? autoTitleCase(value); +} \ No newline at end of file diff --git a/src/pages/datasets/index.module.css b/src/pages/datasets/index.module.css index 50c3ab2..60e625e 100644 --- a/src/pages/datasets/index.module.css +++ b/src/pages/datasets/index.module.css @@ -221,6 +221,11 @@ color: var(--agml-badge-other-fg); } +.badgeVlm { + background: var(--agml-badge-vlm-bg); + color: var(--agml-badge-vlm-fg); +} + .tag { font-family: var(--ifm-code-font-family); font-size: 0.68rem; diff --git a/src/pages/datasets/index.tsx b/src/pages/datasets/index.tsx index a3e71da..63b6fda 100644 --- a/src/pages/datasets/index.tsx +++ b/src/pages/datasets/index.tsx @@ -5,6 +5,7 @@ import { DatasetMetadataModal } from '../../components/DatasetMetadataModal'; import { MultiSelectDropdown } from '../../components/MultiSelectDropdown'; import styles from './index.module.css'; import { computeDatasetStats, filterDatasets, formatDisplayLocation, useDatasets } from '../../lib/datasets'; +import { toDisplayLabel } from '../../lib/labelOverrides'; type FilterKind = 'checkbox' | 'dropdown'; @@ -23,14 +24,14 @@ const DATASET_FILTERS: DatasetFilterConfig[] = [ label: 'Task Type', field: 'machine_learning_task', kind: 'checkbox', - formatOption: (value) => value.replace(/_/g, ' '), + formatOption: (value) => toDisplayLabel(value), }, { key: 'ag_task', label: 'Agricultural Task', field: 'agricultural_task', kind: 'dropdown', - formatOption: (value) => value.replace(/_/g, ' '), + formatOption: (value) => toDisplayLabel(value), }, { key: 'crop_types', @@ -38,7 +39,7 @@ const DATASET_FILTERS: DatasetFilterConfig[] = [ field: 'crop_types', kind: 'dropdown', mode: 'containsAny', - formatOption: (value) => value.replace(/_/g, ' '), + formatOption: (value) => toDisplayLabel(value), }, { key: 'location', @@ -46,28 +47,37 @@ const DATASET_FILTERS: DatasetFilterConfig[] = [ field: 'location', kind: 'dropdown', mode: 'containsAny', - formatOption: (value) => value, - }, - { - key: 'environment', - label: 'Environment', - field: 'environment', - kind: 'checkbox', - formatOption: (value) => value.charAt(0).toUpperCase() + value.slice(1), + formatOption: (value) => toDisplayLabel(value), }, { key: 'platform', label: 'Platform', field: 'platform', kind: 'dropdown', - formatOption: (value) => value, + mode: 'containsAny', + formatOption: (value) => toDisplayLabel(value), + }, + { + key: 'qa_type', + label: 'QA Type', + field: 'qa_type', + kind: 'dropdown', + mode: 'containsAny', + formatOption: (value) => toDisplayLabel(value), + }, + { + key: 'environment', + label: 'Environment', + field: 'environment', + kind: 'checkbox', + formatOption: (value) => toDisplayLabel(value), }, { key: 'real', label: 'Data Type', field: 'real_or_synthetic', kind: 'checkbox', - formatOption: (value) => value, + formatOption: (value) => toDisplayLabel(value), }, { key: 'augmented_counterpart', @@ -75,7 +85,7 @@ const DATASET_FILTERS: DatasetFilterConfig[] = [ field: 'augmented_counterpart', kind: 'checkbox', formatOption: (value) => (value === 'yes' ? 'Yes' : 'No'), - }, + } ]; type FilterKey = (typeof DATASET_FILTERS)[number]['key']; @@ -117,6 +127,7 @@ function formatBytesDecimal(bytes: number | null | undefined) { function taskBadgeClass(task: string | null): string { if (!task) return styles.badgeOther; + if (task.includes('image-text') || task.includes('text-to-text')) return styles.badgeVlm; if (task.includes('classif')) return styles.badgeClassification; if (task.includes('detect')) return styles.badgeDetection; if (task.includes('segment')) return styles.badgeSegmentation; @@ -186,14 +197,20 @@ function DatasetCard({ machine_learning_task, agricultural_task, num_images, + num_rows, zip_size_bytes, augmented_num_images, augmented_zip_size_bytes, location, + dataset_type, } = dataset; const fileSize = formatBytesDecimal(zip_size_bytes); const augmentedFileSize = formatBytesDecimal(augmented_zip_size_bytes); const hasAugmented = augmented_num_images != null; + // VLM datasets are row-based (image/text pairs, conversations) rather than image-count based. + const isVlm = dataset_type === 'vlm'; + const countLabel = isVlm ? 'rows' : 'images'; + const countValue = isVlm ? num_rows : num_images; return ( + + + + +
+ {AXIS_TICKS.map((value) => ( + + {value} + + ))} + {AXIS_TICKS.map((value) => ( + + {value} + + ))} + {projected.map((p) => ( +
+ ))} + {view === '3d' &&

drag to orbit

} +
+ +
+ {legend.map(([cls, color]) => ( + + + {toTitleCase(cls)} + + ))} +
+

+ {hasRealEmbeddings + ? 'projected from embeddings.json' + : `no embeddings.json found for this dataset · showing a seeded demo scatter around class clusters`} +

+
+ ); +} + +function formatRunDate(runId: string): string { + const match = /^(\d{4})(\d{2})(\d{2})/.exec(runId); + if (!match) return runId; + const [, year, month, day] = match; + return `${month}/${day}/${year}`; +} + +function BenchmarkView({ benchmark, embeddings }: { benchmark: BenchmarkData; embeddings: RawEmbedPoint[] | null }) { + const cards = useMemo(() => buildMetricCards(benchmark), [benchmark]); + return ( +
+

Last benchmarking check run: {formatRunDate(benchmark.run_id)}

+
+
+ {cards.map((card) => ( + + ))} +
+ +
+ ); +} + function InlineFilterGroup({ label, options, @@ -172,6 +587,9 @@ export function DatasetMetadataModal({ }, [open, onClose]); const datasetPerformance = useDatasetPerformance(open ? (dataset?.name ?? null) : null); + const { data: benchmark, embeddings } = useBenchmark(open ? (dataset?.name ?? null) : null); + const [showBenchmarks, setShowBenchmarks] = useState(false); + useEffect(() => setShowBenchmarks(false), [dataset?.name, open]); const [metricTypeFilter, setMetricTypeFilter] = useState([]); const [tunedFilter, setTunedFilter] = useState([]); @@ -304,11 +722,23 @@ export function DatasetMetadataModal({ {dataset.real_or_synthetic && {formatValue(dataset.real_or_synthetic)}} - +
+ {benchmark && ( + + )} + +
+
+
{metadataRows.map(([label, value]) => (
@@ -318,6 +748,14 @@ export function DatasetMetadataModal({ ))}
+ {isVlm && dataset.parent_dataset && ( + + )} + {isVlm && dataset.qa_type && dataset.qa_type.length > 0 && (

QA Types

@@ -436,11 +874,6 @@ export function DatasetMetadataModal({ View on Hugging Face )} - {isVlm && dataset.parent_dataset && ( - - View Original Dataset - - )}
)} @@ -585,6 +1018,17 @@ export function DatasetMetadataModal({

No leaderboard results have been submitted for this dataset yet.

)} +
+ + {benchmark && ( +
+ +
+ )} + ); diff --git a/src/components/DatasetTable.module.css b/src/components/DatasetTable.module.css index 7dca769..679329f 100644 --- a/src/components/DatasetTable.module.css +++ b/src/components/DatasetTable.module.css @@ -1,119 +1,865 @@ +.backdrop { + position: fixed; + inset: 0; + z-index: 999; + display: grid; + place-items: center; + padding: 1.25rem; + background: var(--agml-modal-overlay); + backdrop-filter: blur(10px); +} + +.modal { + width: min(1140px, 95vw); + max-height: min(88vh, 920px); + overflow: auto; + border-radius: 12px; + border: 1px solid var(--agml-border-strong); + background: var(--agml-surface-strong); + box-shadow: var(--agml-shadow-strong); + color: var(--agml-text); + padding: 2rem; + perspective: 2000px; +} + +.header { + display: flex; + align-items: flex-start; + justify-content: space-between; + gap: 1rem; + margin-bottom: 1.1rem; +} + +.title { + font-family: var(--ifm-code-font-family); + font-weight: 700; + font-size: 1.25rem; + margin: 0 0 0.5rem; + line-height: 1.2; + overflow-wrap: anywhere; +} + +.headerBadges { + display: flex; + flex-wrap: wrap; + gap: 0.4rem; +} + +.taskBadge { + font-family: var(--ifm-code-font-family); + font-size: 0.65rem; + padding: 0.15rem 0.5rem; + border-radius: 4px; + display: inline-block; + white-space: nowrap; +} + +.badgeClassification { + background: var(--agml-badge-classification-bg); + color: var(--agml-badge-classification-fg); +} + +.badgeDetection { + background: var(--agml-badge-detection-bg); + color: var(--agml-badge-detection-fg); +} + +.badgeSegmentation { + background: var(--agml-badge-segmentation-bg); + color: var(--agml-badge-segmentation-fg); +} + +.badgeOther { + background: var(--agml-badge-other-bg); + color: var(--agml-badge-other-fg); +} + +.tag { + font-family: var(--ifm-code-font-family); + font-size: 0.68rem; + padding: 0.2rem 0.55rem; + border-radius: 4px; + display: inline-block; + white-space: nowrap; + background: var(--agml-tag-bg); + color: var(--agml-tag-fg); +} + +.closeButton { + flex: 0 0 auto; + width: 30px; + height: 30px; + display: flex; + align-items: center; + justify-content: center; + border: 1px solid var(--agml-border-strong); + border-radius: 6px; + background: var(--agml-surface-soft); + color: var(--agml-text); + padding: 0; + font-size: 15px; + line-height: 1; + cursor: pointer; +} + +.closeButton:hover, +.closeButton:focus-visible { + background: var(--agml-border); + outline: none; +} + +.headerActions { + flex: 0 0 auto; + display: flex; + align-items: center; + gap: 0.6rem; +} + +.benchmarkToggle { + flex: 0 0 auto; + border: 1px solid var(--ifm-color-primary); + border-radius: 999px; + background: var(--agml-accent-soft); + color: var(--ifm-color-primary); + padding: 0.5rem 1rem; + font-size: 0.78rem; + font-weight: 600; + cursor: pointer; + white-space: nowrap; +} + +.benchmarkToggle:hover, +.benchmarkToggle:focus-visible { + background: var(--ifm-color-primary); + color: var(--agml-surface-strong); + outline: none; +} + +.flipper { + position: relative; + transform-style: preserve-3d; + transition: transform 0.6s cubic-bezier(0.4, 0, 0.2, 1); +} + +.flipperFlipped { + transform: rotateY(180deg); +} + +.face { + backface-visibility: hidden; +} + +.faceHidden { + visibility: hidden; + pointer-events: none; +} + +.faceBack { + position: absolute; + inset: 0; + transform: rotateY(180deg); +} + +.metricCardGrid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(260px, 1fr)); + gap: 1rem; + margin: 1rem 0 1.5rem; +} + +.metricCard { + padding: 1rem 1.1rem; + border-radius: 10px; + border: 1px solid var(--agml-border); + background: var(--agml-surface-soft); + display: flex; + flex-direction: column; + gap: 0.75rem; +} + +.metricCardHeader { + display: flex; + align-items: center; + justify-content: space-between; + gap: 0.6rem; +} + +.metricCardTitle { + margin: 0; + font-family: var(--ifm-code-font-family); + font-size: 0.72rem; + font-weight: 600; + text-transform: uppercase; + letter-spacing: 0.06em; + color: var(--agml-muted); +} + +.metricCardBadge { + font-family: var(--ifm-code-font-family); + font-size: 0.66rem; + color: var(--agml-muted); + background: var(--agml-tag-bg); + border-radius: 999px; + padding: 0.2rem 0.55rem; + white-space: nowrap; +} + +.metricSkipped { + margin: 0; + font-size: 0.84rem; + font-style: italic; + color: var(--agml-muted); +} + +.metricBars { + display: flex; + flex-direction: column; + gap: 0.5rem; +} + +.metricBarRow { + display: grid; + grid-template-columns: 112px 1fr 56px; + align-items: center; + gap: 0.6rem; +} + +.metricBarLabel { + font-size: 0.8rem; + color: var(--agml-text); + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.metricBarTrack { + height: 8px; + border-radius: 999px; + background: var(--agml-tag-bg); + overflow: hidden; +} + +.metricBarFill { + height: 100%; + border-radius: 999px; + background: var(--ifm-color-primary); +} + +.metricSignedTrack { + position: relative; + height: 8px; + border-radius: 999px; + background: var(--agml-tag-bg); + overflow: hidden; +} + +.metricSignedMidline { + position: absolute; + left: 50%; + top: 0; + bottom: 0; + width: 1px; + background: var(--agml-border-strong); +} + +.metricSignedFill { + position: absolute; + top: 0; + height: 100%; + border-radius: 999px; +} + +.metricBarValue { + font-size: 0.82rem; + font-weight: 600; + color: var(--agml-text); + text-align: right; + white-space: nowrap; +} + +.metricFooterStats { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(110px, 1fr)); + gap: 0.6rem; + padding-top: 0.6rem; + border-top: 1px solid var(--agml-border); +} + +.statTileGrid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(110px, 1fr)); + gap: 0.6rem; +} + +.statTile { + border: 1px solid var(--agml-border); + border-radius: 8px; + padding: 0.65rem 0.8rem; + background: var(--agml-surface-soft); +} + +.statTileLabel { + margin: 0 0 0.3rem; + font-family: var(--ifm-code-font-family); + font-size: 0.62rem; + text-transform: uppercase; + letter-spacing: 0.05em; + color: var(--agml-muted); +} + +.statTileValue { + margin: 0; + font-size: 0.95rem; + font-weight: 600; + color: var(--agml-text); +} + +.embedHeader { + display: flex; + align-items: center; + justify-content: space-between; + gap: 1rem; + flex-wrap: wrap; +} + +.embedSubtitle { + margin: 0.2rem 0 0; + font-size: 0.82rem; + color: var(--agml-muted); +} + +.embedToggle { + display: inline-flex; + border-radius: 999px; + background: var(--agml-tag-bg); + padding: 4px; + gap: 4px; +} + +.embedToggleButton { + border: none; + border-radius: 999px; + padding: 0.4rem 0.9rem; + font-size: 0.82rem; + font-weight: 600; + cursor: pointer; + font-family: inherit; + background: transparent; + color: var(--agml-muted); +} + +.embedToggleActive { + background: var(--ifm-color-primary); + color: var(--agml-surface-strong); +} + +.embedViewport { + position: relative; + margin-top: 0.9rem; + height: 340px; + border-radius: 12px; + border: 1px solid var(--agml-border); + background-color: var(--agml-surface-soft); + background-image: + linear-gradient(var(--agml-border) 1px, transparent 1px), + linear-gradient(90deg, var(--agml-border) 1px, transparent 1px); + background-size: 34px 34px; + background-position: center; + overflow: hidden; +} + +.embedDragHint { + position: absolute; + left: 14px; + bottom: 12px; + margin: 0; + font-size: 0.7rem; + color: var(--agml-muted); + font-family: var(--ifm-code-font-family); +} + +.embedAxisTickX { + position: absolute; + bottom: 4px; + transform: translateX(-50%); + font-family: var(--ifm-code-font-family); + font-size: 0.62rem; + color: var(--agml-muted); + pointer-events: none; +} + +.embedAxisTickY { + position: absolute; + left: 4px; + transform: translateY(-50%); + font-family: var(--ifm-code-font-family); + font-size: 0.62rem; + color: var(--agml-muted); + pointer-events: none; +} + +.embedLegend { + display: flex; + flex-wrap: wrap; + gap: 1rem; + margin-top: 0.9rem; +} + +.embedLegendItem { + display: inline-flex; + align-items: center; + gap: 0.4rem; + font-size: 0.8rem; + color: var(--agml-text); +} + +.embedLegendDot { + display: inline-block; + width: 9px; + height: 9px; + border-radius: 50%; +} + +.embedNote { + margin: 0.9rem 0 0; + font-size: 0.74rem; + color: var(--agml-muted); + font-family: var(--ifm-code-font-family); +} + +.detailGrid { + display: grid; + gap: 0.85rem; + margin: 1.25rem 0 1.75rem; +} + +.detailItem { + min-width: 0; +} + +.detailLabel { + margin: 0 0 0.3rem; + font-family: var(--ifm-code-font-family); + font-size: 0.62rem; + letter-spacing: 0.05em; + text-transform: uppercase; + color: var(--agml-muted); +} + +.detailValue { + margin: 0; + font-size: 0.82rem; + line-height: 1.4; + color: var(--agml-text); + overflow-wrap: anywhere; +} + .section { - margin-top: 1.5rem; - padding: 1.5rem; - border-radius: 20px; - background: var(--agml-surface); - border: 1px solid var(--agml-border); - box-shadow: var(--agml-shadow); - color: var(--agml-text); + margin-bottom: 1.75rem; +} + +.sectionTitle { + font-family: var(--ifm-code-font-family); + font-size: 0.68rem; + font-weight: 600; + text-transform: uppercase; + letter-spacing: 0.05em; + color: var(--agml-muted); + margin: 0 0 0.65rem; +} + +.bodyText { + margin: 0; + color: var(--agml-text); + line-height: 1.5; + overflow-wrap: anywhere; +} + +.bodyText + .bodyText { + margin-top: 0.5rem; +} + +.inlineLabel { + color: var(--agml-muted); + font-weight: 600; +} + +.badgeWrap { + display: flex; + flex-wrap: wrap; + gap: 0.5rem; +} + +.expandButton { + font-family: var(--ifm-code-font-family); + font-size: 0.68rem; + padding: 0.2rem 0.55rem; + border-radius: 4px; + background: transparent; + border: 1px dashed var(--agml-border-strong); + color: var(--agml-muted); + cursor: pointer; +} + +.expandButton:hover, +.expandButton:focus-visible { + border-color: var(--ifm-color-primary); + color: var(--agml-text); + outline: none; +} + +.figure { + margin: 0; +} + +.exampleImage { + display: block; + width: 100%; + max-height: 65vh; + object-fit: contain; + border-radius: 8px; + background: var(--agml-surface-soft); +} + +.snippetRow { + display: flex; + align-items: flex-start; + justify-content: space-between; + gap: 0.75rem; + background: var(--agml-page-bg); + border: 1px solid var(--agml-border); + border-radius: 6px; + padding: 0.6rem 0.85rem; + margin-top: 0.6rem; +} + +.snippetCode { + font-family: var(--ifm-code-font-family); + font-size: 0.78rem; + line-height: 1.6; + color: var(--agml-text); + overflow-x: auto; + white-space: pre; +} + +.snippetCopyButton { + flex-shrink: 0; + align-self: center; + font-family: var(--ifm-code-font-family); + font-size: 0.68rem; + padding: 0.3rem 0.65rem; + border-radius: 5px; + border: 1px solid var(--agml-border-strong); + background: transparent; + color: var(--agml-muted); + cursor: pointer; } -.controls { - display: flex; - flex-wrap: wrap; - align-items: flex-end; - gap: 1rem; - justify-content: space-between; - margin-bottom: 1rem; +.snippetCopyButton:hover, +.snippetCopyButton:focus-visible { + border-color: var(--ifm-color-primary); + color: var(--agml-text); + outline: none; } -.count { - font-weight: 600; - margin: 0; - color: var(--agml-text); +.linkRow { + display: flex; + flex-wrap: wrap; + gap: 0.6rem; + margin-bottom: 1.75rem; } -.helper { - margin: 0.35rem 0 0; - color: var(--agml-muted); - font-size: 0.9rem; +.externalLink, +.hfLink { + display: inline-flex; + width: fit-content; + border-radius: 6px; + padding: 0.5rem 1rem; + border: 1px solid var(--agml-border-strong); + background: var(--agml-tag-bg); + color: var(--agml-tag-fg); + text-decoration: none; + font-size: 0.82rem; + cursor: pointer; } -.searchLabel { - display: flex; - flex-direction: column; - gap: 0.35rem; - font-size: 0.8rem; - text-transform: uppercase; - letter-spacing: 0.08em; - color: var(--agml-muted); +.externalLink:hover, +.externalLink:focus-visible, +.hfLink:hover, +.hfLink:focus-visible { + border-color: var(--ifm-color-primary); + color: var(--ifm-color-primary); + outline: none; } -.searchInput { - min-width: 220px; - border-radius: 999px; - border: 1px solid var(--agml-border-strong); - padding: 0.6rem 0.9rem; - font-size: 0.9rem; - background: var(--agml-surface-strong); - color: var(--agml-text); +.leaderboardWrap { + overflow-x: auto; } -.searchInput:focus { - outline: none; - border-color: var(--ifm-color-primary); - box-shadow: 0 0 0 3px var(--agml-accent-soft); +.filterBar { + display: flex; + flex-wrap: wrap; + align-items: flex-end; + gap: 1rem; + margin-bottom: 0.9rem; + padding: 0.75rem; + border-radius: 8px; + border: 1px solid var(--agml-border); + background: var(--agml-surface-soft); +} + +.filterGroup { + display: flex; + flex-direction: column; + gap: 0.4rem; +} + +.filterGroupLabel { + margin: 0; + font-family: var(--ifm-code-font-family); + font-size: 0.62rem; + text-transform: uppercase; + letter-spacing: 0.04em; + color: var(--agml-muted); +} + +.filterGroupOptions { + display: flex; + flex-wrap: wrap; + gap: 0.65rem; +} + +.filterCheckboxRow { + display: flex; + align-items: center; + gap: 0.35rem; + font-size: 0.75rem; + cursor: pointer; + color: var(--agml-text); + white-space: nowrap; +} + +.checkbox { + appearance: none; + -webkit-appearance: none; + width: 15px; + height: 15px; + flex-shrink: 0; + border-radius: 4px; + border: 1.5px solid var(--agml-border-strong); + background: var(--agml-page-bg); + position: relative; + cursor: pointer; + transition: background 0.12s, border-color 0.12s; +} + +.checkbox:hover { + border-color: var(--agml-muted); +} + +.checkbox:checked { + background: var(--ifm-color-primary); + border-color: var(--ifm-color-primary); +} + +.checkbox::after { + content: ''; + position: absolute; + left: 3.5px; + top: 0.5px; + width: 4px; + height: 7px; + border-style: solid; + border-width: 0 2px 2px 0; + border-color: var(--agml-page-bg); + transform: rotate(45deg); + opacity: 0; +} + +.checkbox:checked::after { + opacity: 1; +} + +.filterRange { + display: flex; + flex-direction: column; + gap: 0.4rem; +} + +.filterRangeLabel { + font-family: var(--ifm-code-font-family); + font-size: 0.62rem; + text-transform: uppercase; + letter-spacing: 0.04em; + color: var(--agml-muted); +} + +.filterRangeInput { + width: 96px; + border-radius: 5px; + border: 1px solid var(--agml-border-strong); + padding: 0.4rem 0.55rem; + background: var(--agml-surface-strong); + color: var(--agml-text); + font-family: var(--ifm-code-font-family); + font-size: 0.75rem; +} + +.clearFiltersButton { + border: 1px solid var(--agml-border-strong); + background: transparent; + border-radius: 999px; + padding: 0.45rem 0.9rem; + font-size: 0.78rem; + color: var(--agml-text); + cursor: pointer; + white-space: nowrap; } .tableWrap { - overflow-x: auto; - border-radius: 16px; - background: var(--agml-surface-strong); - border: 1px solid var(--agml-border); + border: 1px solid var(--agml-border); + border-radius: 8px; + overflow-x: auto; +} + +.leaderboardTable { + width: 100%; + min-width: 960px; + font-size: 0.78rem; +} + +.tableRow { + display: grid; + grid-template-columns: 42px minmax(150px, 1fr) 120px 70px 125px 130px 120px 65px; + gap: 0.5rem; + align-items: start; +} + +.tableRow[role='row']:first-child { + font-family: var(--ifm-code-font-family); + font-size: 0.62rem; + text-transform: uppercase; + letter-spacing: 0.05em; + color: var(--agml-muted); + background: var(--agml-tag-bg); + padding: 0.5rem 0.75rem; + align-items: center; } -.table { - width: 100%; - border-collapse: collapse; - font-size: 0.95rem; +.tableRow:not(:first-child) { + padding: 0.55rem 0.75rem; + border-top: 1px solid var(--agml-border); } -.table th, -.table td { - padding: 0.8rem 1rem; - border-bottom: 1px solid var(--agml-border); - text-align: left; +.tableRow > [role='columnheader'] { + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; } -.datasetButton { - appearance: none; - border: none; - background: transparent; - color: var(--ifm-color-primary); - padding: 0; - font: inherit; - cursor: pointer; - text-align: left; +.simpleCell { + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + display: block; } -.datasetButton:hover, -.datasetButton:focus-visible { - text-decoration: underline; +.rankCell { + font-family: var(--ifm-code-font-family); + color: var(--agml-muted); } -.datasetButton:focus-visible { - outline: 2px solid var(--ifm-color-primary); - outline-offset: 3px; - border-radius: 4px; +.modelButton { + background: none; + border: none; + padding: 0; + max-width: 100%; + font-family: var(--ifm-code-font-family); + font-weight: 500; + color: var(--agml-text); + text-decoration: underline dashed var(--agml-muted); + text-decoration-thickness: 1px; + text-underline-offset: 3px; + cursor: pointer; + text-align: left; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + display: block; } -.table th { - font-size: 0.75rem; - text-transform: uppercase; - letter-spacing: 0.08em; - color: var(--agml-muted); - background: var(--agml-surface-soft); +.scoresCell { + display: flex; + flex-direction: column; + gap: 0.15rem; + font-family: var(--ifm-code-font-family); + font-size: 0.72rem; + color: var(--ifm-color-primary); } -.num { - text-align: right; - font-variant-numeric: tabular-nums; +.scoresCell span { + white-space: nowrap; } -.status { - color: var(--agml-muted); +.stackCell { + display: flex; + flex-direction: column; + gap: 0.15rem; + font-family: var(--ifm-code-font-family); + font-size: 0.72rem; + color: var(--agml-muted); } -:global(html[data-theme='dark']) .tableWrap { - background: var(--agml-surface-strong); +.stackCell span { + white-space: nowrap; +} + +.notesRow { + padding: 0.5rem 0.75rem 0.75rem 2.6rem; + background: var(--agml-surface-soft); + border-top: 1px dashed var(--agml-border); + font-size: 0.75rem; + line-height: 1.5; +} + +.notesLabel { + margin: 0 0 0.2rem; + font-family: var(--ifm-code-font-family); + font-size: 0.62rem; + text-transform: uppercase; + letter-spacing: 0.05em; + color: var(--agml-muted); +} + +.notesText { + margin: 0; + color: var(--agml-text); + line-height: 1.5; + overflow-wrap: anywhere; +} + +.leaderboardTable a { + color: var(--ifm-color-primary); + font-weight: 600; + text-decoration: none; +} + +.leaderboardTable a:hover, +.leaderboardTable a:focus-visible { + text-decoration: underline; +} + +@media (max-width: 720px) { + .modal { + padding: 1.25rem; + border-radius: 10px; + } + + .header { + flex-direction: column; + } + + .detailGrid { + grid-template-columns: repeat(2, 1fr) !important; + } } diff --git a/src/lib/benchmarks.ts b/src/lib/benchmarks.ts new file mode 100644 index 0000000..c4b6225 --- /dev/null +++ b/src/lib/benchmarks.ts @@ -0,0 +1,160 @@ +// Dataset benchmarking metrics, loaded on demand from static/data/benchmarking-result/.json +// (+ an optional .embeddings.json for the UMAP scatter). One task type's metric shape is +// defined below; other task types will get their own shape as their benchmarking pipelines ship. +import { useEffect, useState } from "react"; +import useBaseUrl from "@docusaurus/useBaseUrl"; + +export interface ClassImbalanceMetrics { + counts: Record; + imbalance_ratio: number; + normalized_entropy: number; + most_frequent_class: string; + least_frequent_class: string; + total_train_examples: number; +} + +export interface ExactDuplicateMetrics { + total_images: number; + exact_duplicate_count: number; + exact_duplicate_rate: number; + duplicate_groups: number; + cross_split_duplicates: number; +} + +export interface ResolutionStat { + mean: number; + std: number; + min: number; + max: number; +} + +export interface ResolutionConsistencyMetrics { + total_images: number; + width: ResolutionStat; + height: ResolutionStat; + aspect_ratio: ResolutionStat; + area_cv: number; + mode_distribution: Record; +} + +export interface NearDuplicateMetrics { + total_images: number; + embed_model: string; + threshold: number; + near_duplicate_count: number; + near_duplicate_rate: number; + near_duplicate_groups: number; + cross_split_near_duplicates: number; + faiss_index_type: string; +} + +export interface FeatureSeparabilityMetrics { + embed_model: string; + n_total: number; + n_silhouette_samples: number; + silhouette_score: number; + silhouette_interpretation: string; + davies_bouldin_index: number; + davies_bouldin_interpretation: string; + per_class_silhouette: Record; +} + +export interface IntraClassDiversityMetrics { + embed_model: string; + per_class_diversity: Record; + mean_diversity: number; + min_diversity_class: string; + max_diversity_class: string; +} + +export interface ImageClassificationBenchmark { + dataset: string; + run_id: string; + phases_completed: number[]; + metrics: { + class_imbalance?: ClassImbalanceMetrics; + exact_duplicate?: ExactDuplicateMetrics; + resolution_consistency?: ResolutionConsistencyMetrics; + metadata_coverage?: { skipped: boolean; reason?: string }; + near_duplicate?: NearDuplicateMetrics; + feature_separability?: FeatureSeparabilityMetrics; + intra_class_diversity?: IntraClassDiversityMetrics; + }; +} + +// Only image-classification benchmarks exist today; other task types will get their own +// metric shapes later. +export type BenchmarkData = ImageClassificationBenchmark; + +export interface EmbedPoint { + x: number; + y: number; + z: number; + class: string; +} + +interface BenchmarkState { + data: BenchmarkData | null; + embeddings: EmbedPoint[] | null; + loading: boolean; +} + +const EMPTY_STATE: BenchmarkState = { + data: null, + embeddings: null, + loading: false, +}; + +async function fetchJson(url: string): Promise { + try { + const response = await fetch(url); + if (!response.ok) return null; + return (await response.json()) as T; + } catch { + return null; + } +} + +// Fetches static/data/benchmarking-result/.json (metrics) and, if present, +// .embeddings.json (UMAP points). Returns null data when no benchmark file exists +// for this dataset (a plain 404, not an error state). +export function useBenchmark(datasetName: string | null): BenchmarkState { + const metricsUrl = useBaseUrl( + `/data/benchmarking-result/${datasetName ?? "_none"}.json`, + ); + const embeddingsUrl = useBaseUrl( + `/data/benchmarking-result/${datasetName ?? "_none"}.embeddings.json`, + ); + const [state, setState] = useState(EMPTY_STATE); + + useEffect(() => { + if (!datasetName) { + setState(EMPTY_STATE); + return; + } + let cancelled = false; + setState({ data: null, embeddings: null, loading: true }); + + fetchJson(metricsUrl).then((data) => { + if (cancelled) return; + if (!data) { + setState({ data: null, embeddings: null, loading: false }); + return; + } + fetchJson(embeddingsUrl).then((embeddings) => { + if (cancelled) return; + setState({ + data, + embeddings: embeddings ?? null, + loading: false, + }); + }); + }); + + return () => { + cancelled = true; + }; + }, [datasetName, metricsUrl, embeddingsUrl]); + + return state; +} diff --git a/src/lib/datasets.ts b/src/lib/datasets.ts index 10f298f..e48d753 100644 --- a/src/lib/datasets.ts +++ b/src/lib/datasets.ts @@ -1,426 +1,581 @@ -import { useEffect, useMemo, useState } from 'react'; -import useBaseUrl from '@docusaurus/useBaseUrl'; -import { toDisplayLabel } from './labelOverrides'; +import { useEffect, useMemo, useState } from "react"; +import useBaseUrl from "@docusaurus/useBaseUrl"; +import { toDisplayLabel } from "./labelOverrides"; export interface Dataset { - name: string; - source: 'agml' | 'huggingface' | string | null; - machine_learning_task: string | null; - agricultural_task: string | null; - location: string | string[] | null; - environment: string | null; - augmented_counterpart: string | null; - crop_types: string[] | null; - sensor_modality: string | null; - real_or_synthetic: string | null; - platform: string[] | null; - input_data_format: string | null; - annotation_format: string | null; - num_images: number | null; - augmented_num_images: number | null; - augmented_zip_size_bytes: number | null; - documentation: string | null; - classes: string | null; - stats_mean: number[] | null; - stats_std: number[] | null; - examples_image_url: string | null; - license: string | null; - citation: string | null; - parent_dataset?: string | null; - zip_size_bytes?: number | null; - hf_link?: string | null; - // VLM (vision-language) dataset fields — absent/null on plain vision datasets. - dataset_type: 'vision' | 'vlm'; - qa_type: string[] | null; - task_dimensions: string[] | null; - num_task_types: number | null; - conversation_format: string | null; - num_rows: number | null; - source_datasets: string[] | null; + name: string; + source: "agml" | "huggingface" | string | null; + machine_learning_task: string | null; + agricultural_task: string | null; + location: string | string[] | null; + environment: string | null; + augmented_counterpart: string | null; + crop_types: string[] | null; + sensor_modality: string | null; + real_or_synthetic: string | null; + platform: string[] | null; + input_data_format: string | null; + annotation_format: string | null; + num_images: number | null; + augmented_num_images: number | null; + augmented_zip_size_bytes: number | null; + documentation: string | null; + classes: string | null; + stats_mean: number[] | null; + stats_std: number[] | null; + examples_image_url: string | null; + license: string | null; + citation: string | null; + parent_dataset?: string | null; + zip_size_bytes?: number | null; + hf_link?: string | null; + // VLM (vision-language) dataset fields — absent/null on plain vision datasets. + dataset_type: "vision" | "vlm"; + qa_type: string[] | null; + task_dimensions: string[] | null; + num_task_types: number | null; + conversation_format: string | null; + num_rows: number | null; + source_datasets: string[] | null; } type DatasetRecord = Record; export type DatasetFieldFilter = { - field: keyof Dataset; - values?: string[]; - mode?: 'exact' | 'containsAny'; + field: keyof Dataset; + values?: string[]; + mode?: "exact" | "containsAny"; }; -const DATASET_MANIFESTS = ['/data/datasets.json', '/data/hf_datasets.json'] as const; +const DATASET_MANIFESTS = [ + "/data/datasets.json", + "/data/hf_datasets.json", +] as const; function isRecord(value: unknown): value is DatasetRecord { - return typeof value === 'object' && value !== null && !Array.isArray(value); + return typeof value === "object" && value !== null && !Array.isArray(value); } function firstString(...values: unknown[]): string | null { - for (const value of values) { - if (typeof value === 'string') { - const trimmed = value.trim(); - if (trimmed) return trimmed; + for (const value of values) { + if (typeof value === "string") { + const trimmed = value.trim(); + if (trimmed) return trimmed; + } } - } - return null; + return null; } function toNumber(value: unknown): number | null { - if (typeof value === 'number' && Number.isFinite(value)) return value; - if (typeof value === 'string' && value.trim()) { - const parsed = Number(value.replace(/,/g, '')); - return Number.isFinite(parsed) ? parsed : null; - } - return null; + if (typeof value === "number" && Number.isFinite(value)) return value; + if (typeof value === "string" && value.trim()) { + const parsed = Number(value.replace(/,/g, "")); + return Number.isFinite(parsed) ? parsed : null; + } + return null; } function toNumberArray(value: unknown): number[] | null { - if (!Array.isArray(value)) return null; - const numbers = value.map((entry) => toNumber(entry)); - return numbers.every((entry) => entry != null) ? (numbers as number[]) : null; + if (!Array.isArray(value)) return null; + const numbers = value.map((entry) => toNumber(entry)); + return numbers.every((entry) => entry != null) + ? (numbers as number[]) + : null; } function toText(value: unknown): string | null { - if (typeof value === 'string') { - const trimmed = value.trim(); - return trimmed ? trimmed : null; - } - if (typeof value === 'number' || typeof value === 'boolean') return String(value); - if (Array.isArray(value)) { - const parts = value.map((entry) => toText(entry)).filter((entry): entry is string => entry != null); - return parts.length ? parts.join(', ') : null; - } - if (isRecord(value)) { - const parts = Object.entries(value) - .map(([key, entry]) => { - const text = toText(entry); - return text ? `${key}: ${text}` : null; - }) - .filter((entry): entry is string => entry != null); - return parts.length ? parts.join('; ') : null; - } - return null; + if (typeof value === "string") { + const trimmed = value.trim(); + return trimmed ? trimmed : null; + } + if (typeof value === "number" || typeof value === "boolean") + return String(value); + if (Array.isArray(value)) { + const parts = value + .map((entry) => toText(entry)) + .filter((entry): entry is string => entry != null); + return parts.length ? parts.join(", ") : null; + } + if (isRecord(value)) { + const parts = Object.entries(value) + .map(([key, entry]) => { + const text = toText(entry); + return text ? `${key}: ${text}` : null; + }) + .filter((entry): entry is string => entry != null); + return parts.length ? parts.join("; ") : null; + } + return null; } function toStringArray(value: unknown): string[] | null { - if (!Array.isArray(value)) return null; - const parts = value - .map((entry) => firstString(entry)) - .filter((entry): entry is string => entry != null); - return parts.length ? parts : null; + if (!Array.isArray(value)) return null; + const parts = value + .map((entry) => firstString(entry)) + .filter((entry): entry is string => entry != null); + return parts.length ? parts : null; } function normalizeLocation(value: unknown): string | string[] | null { - if (Array.isArray(value)) return toStringArray(value); - return firstString(value); + if (Array.isArray(value)) return toStringArray(value); + return firstString(value); } function normalizeDataset(raw: unknown): Dataset | null { - if (!isRecord(raw)) return null; - - const stats = isRecord(raw.stats) ? raw.stats : null; - const name = firstString(raw.name, raw.dataset, raw.slug, raw.id, raw.key); - if (!name) return null; - - const augmentedNumImages = toNumber(raw.augmented_num_images ?? raw.augmented_n_images ?? raw.augmented_image_count); - const augmentedCounterpart = - firstString(raw.augmented_counterpart) ?? (augmentedNumImages != null ? 'yes' : 'no'); - - const mlTask = firstString(raw.machine_learning_task, raw.ml_task, raw.task); - const qaType = toStringArray(raw.qa_type); - const taskDimensions = toStringArray(raw.task_dimensions); - const isVlm = mlTask === 'image-text-to-text' || Boolean(qaType?.length) || Boolean(taskDimensions?.length); - - return { - name, - machine_learning_task: mlTask, - source: firstString(raw.source), - agricultural_task: firstString(raw.agricultural_task, raw.ag_task), - location: normalizeLocation(raw.location), - environment: firstString(raw.environment, raw.env, raw.image_environment)?.toLowerCase() ?? null, - augmented_counterpart: augmentedCounterpart, - crop_types: toStringArray(raw.crop_types ?? raw.cropType ?? raw.crop_type) - ?.map((c) => c.trim()) - .filter((c) => c && !/^\+.*\b(more|others?)\b/i.test(c)) ?? null, - sensor_modality: firstString(raw.sensor_modality, raw.sensor, raw.modality) ?? toStringArray(raw.sensor_modality)?.join(', ') ?? null, - real_or_synthetic: firstString(raw.real_or_synthetic, raw.real_synthetic), - platform: parsePlatform(raw.platform), - input_data_format: firstString(raw.input_data_format, raw.input_format), - annotation_format: firstString(raw.annotation_format), - num_images: toNumber(raw.num_images ?? raw.n_images ?? raw.image_count), - augmented_num_images: augmentedNumImages, - augmented_zip_size_bytes: toNumber(raw.augmented_zip_size_bytes ?? raw.augmentedZipSizeBytes), - documentation: firstString(raw.documentation, raw.docs_url, raw.doc_url, raw.url), - classes: toText(raw.classes), - stats_mean: toNumberArray(raw.stats_mean ?? stats?.mean), - stats_std: toNumberArray(raw.stats_std ?? stats?.std), - examples_image_url: firstString(raw.examples_image_url, raw.example_image_url, raw.examples_url, raw.image_url), - license: firstString(raw.license), - citation: firstString(raw.citation), - parent_dataset: firstString(raw.parent_dataset, raw.parentDataset), - zip_size_bytes: toNumber(raw.zip_size_bytes ?? raw.zipSizeBytes), - hf_link: firstString(raw.hf_link, raw.huggingface_link, raw.hf_url), - dataset_type: isVlm ? 'vlm' : 'vision', - qa_type: qaType, - task_dimensions: taskDimensions, - num_task_types: toNumber(raw.num_task_types), - conversation_format: firstString(raw.conversation_format), - num_rows: toNumber(raw.num_rows), - source_datasets: toStringArray((raw.composition as Record | undefined)?.source_datasets), - }; + if (!isRecord(raw)) return null; + + const stats = isRecord(raw.stats) ? raw.stats : null; + const name = firstString(raw.name, raw.dataset, raw.slug, raw.id, raw.key); + if (!name) return null; + + const augmentedNumImages = toNumber( + raw.augmented_num_images ?? + raw.augmented_n_images ?? + raw.augmented_image_count, + ); + const augmentedCounterpart = + firstString(raw.augmented_counterpart) ?? + (augmentedNumImages != null ? "yes" : "no"); + + const mlTask = firstString( + raw.machine_learning_task, + raw.ml_task, + raw.task, + ); + const qaType = toStringArray(raw.qa_type); + const taskDimensions = toStringArray(raw.task_dimensions); + const isVlm = + mlTask === "image-text-to-text" || + Boolean(qaType?.length) || + Boolean(taskDimensions?.length); + + return { + name, + machine_learning_task: mlTask, + source: firstString(raw.source), + agricultural_task: firstString(raw.agricultural_task, raw.ag_task), + location: normalizeLocation(raw.location), + environment: + firstString( + raw.environment, + raw.env, + raw.image_environment, + )?.toLowerCase() ?? null, + augmented_counterpart: augmentedCounterpart, + crop_types: + toStringArray(raw.crop_types ?? raw.cropType ?? raw.crop_type) + ?.map((c) => c.trim()) + .filter((c) => c && !/^\+.*\b(more|others?)\b/i.test(c)) ?? + null, + sensor_modality: + firstString(raw.sensor_modality, raw.sensor, raw.modality) ?? + toStringArray(raw.sensor_modality)?.join(", ") ?? + null, + real_or_synthetic: firstString( + raw.real_or_synthetic, + raw.real_synthetic, + ), + platform: parsePlatform(raw.platform), + input_data_format: firstString(raw.input_data_format, raw.input_format), + annotation_format: firstString(raw.annotation_format), + num_images: toNumber(raw.num_images ?? raw.n_images ?? raw.image_count), + augmented_num_images: augmentedNumImages, + augmented_zip_size_bytes: toNumber( + raw.augmented_zip_size_bytes ?? raw.augmentedZipSizeBytes, + ), + documentation: firstString( + raw.documentation, + raw.docs_url, + raw.doc_url, + raw.url, + ), + classes: toText(raw.classes), + stats_mean: toNumberArray(raw.stats_mean ?? stats?.mean), + stats_std: toNumberArray(raw.stats_std ?? stats?.std), + examples_image_url: firstString( + raw.examples_image_url, + raw.example_image_url, + raw.examples_url, + raw.image_url, + ), + license: firstString(raw.license), + citation: firstString(raw.citation), + parent_dataset: firstString(raw.parent_dataset, raw.parentDataset), + zip_size_bytes: toNumber(raw.zip_size_bytes ?? raw.zipSizeBytes), + hf_link: firstString(raw.hf_link, raw.huggingface_link, raw.hf_url), + dataset_type: isVlm ? "vlm" : "vision", + qa_type: qaType, + task_dimensions: taskDimensions, + num_task_types: toNumber(raw.num_task_types), + conversation_format: firstString(raw.conversation_format), + num_rows: toNumber(raw.num_rows), + source_datasets: toStringArray( + (raw.composition as Record | undefined) + ?.source_datasets, + ), + }; } function mergeDataset(current: Dataset, incoming: Dataset): Dataset { - return { - name: current.name, - machine_learning_task: current.machine_learning_task ?? incoming.machine_learning_task, - source: current.source ?? incoming.source, - agricultural_task: current.agricultural_task ?? incoming.agricultural_task, - location: current.location ?? incoming.location, - environment: current.environment ?? incoming.environment, - augmented_counterpart: current.augmented_counterpart ?? incoming.augmented_counterpart, - crop_types: current.crop_types ?? incoming.crop_types, - sensor_modality: current.sensor_modality ?? incoming.sensor_modality, - real_or_synthetic: current.real_or_synthetic ?? incoming.real_or_synthetic, - platform: (current.platform || incoming.platform) - ? Array.from(new Set([...(current.platform ?? []), ...(incoming.platform ?? [])])) - : null, - input_data_format: current.input_data_format ?? incoming.input_data_format, - annotation_format: current.annotation_format ?? incoming.annotation_format, - num_images: current.num_images ?? incoming.num_images, - augmented_num_images: current.augmented_num_images ?? incoming.augmented_num_images, - augmented_zip_size_bytes: current.augmented_zip_size_bytes ?? incoming.augmented_zip_size_bytes, - documentation: current.documentation ?? incoming.documentation, - classes: current.classes ?? incoming.classes, - stats_mean: current.stats_mean ?? incoming.stats_mean, - stats_std: current.stats_std ?? incoming.stats_std, - examples_image_url: current.examples_image_url ?? incoming.examples_image_url, - license: current.license ?? incoming.license, - citation: current.citation ?? incoming.citation, - parent_dataset: current.parent_dataset ?? incoming.parent_dataset, - zip_size_bytes: current.zip_size_bytes ?? incoming.zip_size_bytes, - hf_link: current.hf_link ?? incoming.hf_link, - dataset_type: current.dataset_type === 'vlm' || incoming.dataset_type === 'vlm' ? 'vlm' : 'vision', - qa_type: current.qa_type ?? incoming.qa_type, - task_dimensions: current.task_dimensions ?? incoming.task_dimensions, - num_task_types: current.num_task_types ?? incoming.num_task_types, - conversation_format: current.conversation_format ?? incoming.conversation_format, - num_rows: current.num_rows ?? incoming.num_rows, - source_datasets: current.source_datasets ?? incoming.source_datasets, - }; + return { + name: current.name, + machine_learning_task: + current.machine_learning_task ?? incoming.machine_learning_task, + source: current.source ?? incoming.source, + agricultural_task: + current.agricultural_task ?? incoming.agricultural_task, + location: current.location ?? incoming.location, + environment: current.environment ?? incoming.environment, + augmented_counterpart: + current.augmented_counterpart ?? incoming.augmented_counterpart, + crop_types: current.crop_types ?? incoming.crop_types, + sensor_modality: current.sensor_modality ?? incoming.sensor_modality, + real_or_synthetic: + current.real_or_synthetic ?? incoming.real_or_synthetic, + platform: + current.platform || incoming.platform + ? Array.from( + new Set([ + ...(current.platform ?? []), + ...(incoming.platform ?? []), + ]), + ) + : null, + input_data_format: + current.input_data_format ?? incoming.input_data_format, + annotation_format: + current.annotation_format ?? incoming.annotation_format, + num_images: current.num_images ?? incoming.num_images, + augmented_num_images: + current.augmented_num_images ?? incoming.augmented_num_images, + augmented_zip_size_bytes: + current.augmented_zip_size_bytes ?? + incoming.augmented_zip_size_bytes, + documentation: current.documentation ?? incoming.documentation, + classes: current.classes ?? incoming.classes, + stats_mean: current.stats_mean ?? incoming.stats_mean, + stats_std: current.stats_std ?? incoming.stats_std, + examples_image_url: + current.examples_image_url ?? incoming.examples_image_url, + license: current.license ?? incoming.license, + citation: current.citation ?? incoming.citation, + parent_dataset: current.parent_dataset ?? incoming.parent_dataset, + zip_size_bytes: current.zip_size_bytes ?? incoming.zip_size_bytes, + hf_link: current.hf_link ?? incoming.hf_link, + dataset_type: + current.dataset_type === "vlm" || incoming.dataset_type === "vlm" + ? "vlm" + : "vision", + qa_type: current.qa_type ?? incoming.qa_type, + task_dimensions: current.task_dimensions ?? incoming.task_dimensions, + num_task_types: current.num_task_types ?? incoming.num_task_types, + conversation_format: + current.conversation_format ?? incoming.conversation_format, + num_rows: current.num_rows ?? incoming.num_rows, + source_datasets: current.source_datasets ?? incoming.source_datasets, + }; } function normalizeManifest(json: unknown): Dataset[] { - const records = Array.isArray(json) ? json : isRecord(json) ? Object.values(json) : []; - return records.map(normalizeDataset).filter((entry): entry is Dataset => entry != null); + const records = Array.isArray(json) + ? json + : isRecord(json) + ? Object.values(json) + : []; + return records + .map(normalizeDataset) + .filter((entry): entry is Dataset => entry != null); } async function fetchManifest(manifestUrl: string): Promise { - const response = await fetch(manifestUrl); - if (!response.ok) { - throw new Error(`Failed to load ${manifestUrl}`); - } - return normalizeManifest(await response.json()); + const response = await fetch(manifestUrl); + if (!response.ok) { + throw new Error(`Failed to load ${manifestUrl}`); + } + return normalizeManifest(await response.json()); } export async function loadDatasets(manifestUrls: string[]): Promise { - const results = await Promise.allSettled(manifestUrls.map((manifestUrl) => fetchManifest(manifestUrl))); - const merged = new Map(); - let loadedAny = false; - - for (const result of results) { - if (result.status !== 'fulfilled') continue; - loadedAny = true; - for (const entry of result.value) { - const existing = merged.get(entry.name); - merged.set(entry.name, existing ? mergeDataset(existing, entry) : entry); + const results = await Promise.allSettled( + manifestUrls.map((manifestUrl) => fetchManifest(manifestUrl)), + ); + const merged = new Map(); + let loadedAny = false; + + for (const result of results) { + if (result.status !== "fulfilled") continue; + loadedAny = true; + for (const entry of result.value) { + const existing = merged.get(entry.name); + merged.set( + entry.name, + existing ? mergeDataset(existing, entry) : entry, + ); + } } - } - if (loadedAny) { - return Array.from(merged.values()).sort((a, b) => a.name.localeCompare(b.name)); - } + if (loadedAny) { + return Array.from(merged.values()).sort((a, b) => + a.name.localeCompare(b.name), + ); + } - const firstError = results.find((result): result is PromiseRejectedResult => result.status === 'rejected'); - throw firstError?.reason instanceof Error ? firstError.reason : new Error('Failed to load datasets'); + const firstError = results.find( + (result): result is PromiseRejectedResult => + result.status === "rejected", + ); + throw firstError?.reason instanceof Error + ? firstError.reason + : new Error("Failed to load datasets"); } -export function useDatasets(): { data: Dataset[]; loading: boolean; error: Error | null } { - const [data, setData] = useState([]); - const [loading, setLoading] = useState(true); - const [error, setError] = useState(null); - const resolvedManifestUrls = DATASET_MANIFESTS.map((manifest) => useBaseUrl(manifest)); - // Memoized off the individual (stable) resolved URLs rather than reusing the array - // from .map() above, which is a new reference every render and would otherwise - // retrigger the effect below on every single render — an infinite fetch loop. - const manifestUrls = useMemo(() => resolvedManifestUrls, resolvedManifestUrls); - - useEffect(() => { - let active = true; - loadDatasets(manifestUrls) - .then((next) => { - if (!active) return; - setData(next); - }) - .catch((err) => { - if (!active) return; - setError(err instanceof Error ? err : new Error('Failed to load datasets')); - }) - .finally(() => { - if (!active) return; - setLoading(false); - }); - - return () => { - active = false; - }; - }, [manifestUrls]); - - return { data, loading, error }; +export function useDatasets(): { + data: Dataset[]; + loading: boolean; + error: Error | null; +} { + const [data, setData] = useState([]); + const [loading, setLoading] = useState(true); + const [error, setError] = useState(null); + const resolvedManifestUrls = DATASET_MANIFESTS.map((manifest) => + useBaseUrl(manifest), + ); + // Memoized off the individual (stable) resolved URLs rather than reusing the array + // from .map() above, which is a new reference every render and would otherwise + // retrigger the effect below on every single render — an infinite fetch loop. + const manifestUrls = useMemo( + () => resolvedManifestUrls, + resolvedManifestUrls, + ); + + useEffect(() => { + let active = true; + loadDatasets(manifestUrls) + .then((next) => { + if (!active) return; + setData(next); + }) + .catch((err) => { + if (!active) return; + setError( + err instanceof Error + ? err + : new Error("Failed to load datasets"), + ); + }) + .finally(() => { + if (!active) return; + setLoading(false); + }); + + return () => { + active = false; + }; + }, [manifestUrls]); + + return { data, loading, error }; } -function matchesFieldValue(value: string | string[] | null | undefined, values: string[], mode: 'exact' | 'containsAny') { - if (value == null || values.length === 0) return false; - if (Array.isArray(value)) { - return mode === 'containsAny' - ? value.some((entry) => values.includes(entry)) - : value.some((entry) => values.includes(entry)); - } - if (mode === 'containsAny') { - return values.some((entry) => value.toLowerCase().includes(entry.toLowerCase())); - } - return values.includes(value); +function matchesFieldValue( + value: string | string[] | null | undefined, + values: string[], + mode: "exact" | "containsAny", +) { + if (value == null || values.length === 0) return false; + if (Array.isArray(value)) { + return mode === "containsAny" + ? value.some((entry) => values.includes(entry)) + : value.some((entry) => values.includes(entry)); + } + if (mode === "containsAny") { + return values.some((entry) => + value.toLowerCase().includes(entry.toLowerCase()), + ); + } + return values.includes(value); } export function filterDatasets( - datasets: Dataset[], - opts: { - q?: string; - fieldFilters?: DatasetFieldFilter[]; - mlTasks?: string[]; - agTasks?: string[]; - platforms?: string[]; - realOrSynthetic?: string[]; - includeChildren?: boolean; - } + datasets: Dataset[], + opts: { + q?: string; + fieldFilters?: DatasetFieldFilter[]; + mlTasks?: string[]; + agTasks?: string[]; + platforms?: string[]; + realOrSynthetic?: string[]; + includeChildren?: boolean; + }, ): Dataset[] { - let out = datasets; - - if (opts.includeChildren === false) { - out = out.filter((d) => !d.parent_dataset); - } - - if (opts.q?.trim()) { - const q = opts.q.trim().toLowerCase(); - out = out.filter((d) => { - const locationMatches = Array.isArray(d.location) - ? d.location.some((entry) => entry.toLowerCase().includes(q)) - : d.location?.toLowerCase().includes(q) ?? false; - const cropMatches = d.crop_types?.some((entry) => entry.toLowerCase().includes(q)) ?? false; - return ( - d.name.toLowerCase().includes(q) || - d.agricultural_task?.toLowerCase().includes(q) || - d.machine_learning_task?.toLowerCase().includes(q) || - d.environment?.toLowerCase().includes(q) || - d.augmented_counterpart?.toLowerCase().includes(q) || - d.platform?.some((p) => p.toLowerCase().includes(q)) || - locationMatches || - cropMatches - ); - }); - } - - for (const filter of opts.fieldFilters ?? []) { - if (!filter.values?.length) continue; - out = out.filter((dataset) => matchesFieldValue(dataset[filter.field] as string | string[] | null | undefined, filter.values!, filter.mode ?? 'exact')); - } - - if (opts.mlTasks?.length) { - out = out.filter((d) => d.machine_learning_task != null && opts.mlTasks!.includes(d.machine_learning_task)); - } - if (opts.agTasks?.length) { - out = out.filter((d) => d.agricultural_task != null && opts.agTasks!.includes(d.agricultural_task)); - } - if (opts.platforms?.length) { - out = out.filter((d) => d.platform != null && d.platform.some((p) => opts.platforms!.includes(p))); - } - if (opts.realOrSynthetic?.length) { - out = out.filter((d) => d.real_or_synthetic != null && opts.realOrSynthetic!.includes(d.real_or_synthetic)); - } - - return out; + let out = datasets; + + if (opts.includeChildren === false) { + out = out.filter((d) => !d.parent_dataset); + } + + if (opts.q?.trim()) { + const q = opts.q.trim().toLowerCase(); + out = out.filter((d) => { + const locationMatches = Array.isArray(d.location) + ? d.location.some((entry) => entry.toLowerCase().includes(q)) + : (d.location?.toLowerCase().includes(q) ?? false); + const cropMatches = + d.crop_types?.some((entry) => + entry.toLowerCase().includes(q), + ) ?? false; + return ( + d.name.toLowerCase().includes(q) || + d.agricultural_task?.toLowerCase().includes(q) || + d.machine_learning_task?.toLowerCase().includes(q) || + d.environment?.toLowerCase().includes(q) || + d.augmented_counterpart?.toLowerCase().includes(q) || + d.platform?.some((p) => p.toLowerCase().includes(q)) || + locationMatches || + cropMatches + ); + }); + } + + for (const filter of opts.fieldFilters ?? []) { + if (!filter.values?.length) continue; + out = out.filter((dataset) => + matchesFieldValue( + dataset[filter.field] as string | string[] | null | undefined, + filter.values!, + filter.mode ?? "exact", + ), + ); + } + + if (opts.mlTasks?.length) { + out = out.filter( + (d) => + d.machine_learning_task != null && + opts.mlTasks!.includes(d.machine_learning_task), + ); + } + if (opts.agTasks?.length) { + out = out.filter( + (d) => + d.agricultural_task != null && + opts.agTasks!.includes(d.agricultural_task), + ); + } + if (opts.platforms?.length) { + out = out.filter( + (d) => + d.platform != null && + d.platform.some((p) => opts.platforms!.includes(p)), + ); + } + if (opts.realOrSynthetic?.length) { + out = out.filter( + (d) => + d.real_or_synthetic != null && + opts.realOrSynthetic!.includes(d.real_or_synthetic), + ); + } + + return out; } export function useDatasetOptions(data: Dataset[]) { - return useMemo(() => { - const unique = (values: Array, sort = true) => { - const flattened = values.flatMap((value) => (Array.isArray(value) ? value : value != null ? [value] : [])); - const set = new Set(flattened.filter((value) => Boolean(value && String(value).trim()))); - const list = Array.from(set); - if (sort) list.sort((a, b) => a.localeCompare(b)); - return list; - }; - - return { - mlTasks: unique(data.map((d) => d.machine_learning_task)), - agTasks: unique(data.map((d) => d.agricultural_task)), - environments: unique(data.map((d) => d.environment)), - augmentedCounterparts: unique(data.map((d) => d.augmented_counterpart)), - cropTypes: unique(data.flatMap((d) => d.crop_types ?? [])), - locations: unique(data.map((d) => d.location)), - platforms: unique(data.flatMap((d) => d.platform ?? [])), - realOptions: unique(data.map((d) => d.real_or_synthetic)), - datasetTypes: unique(data.map((d) => d.dataset_type)), - qaTypes: unique(data.flatMap((d) => d.qa_type ?? [])), - taskDimensions: unique(data.flatMap((d) => d.task_dimensions ?? [])), - }; - }, [data]); + return useMemo(() => { + const unique = ( + values: Array, + sort = true, + ) => { + const flattened = values.flatMap((value) => + Array.isArray(value) ? value : value != null ? [value] : [], + ); + const set = new Set( + flattened.filter((value) => + Boolean(value && String(value).trim()), + ), + ); + const list = Array.from(set); + if (sort) list.sort((a, b) => a.localeCompare(b)); + return list; + }; + + return { + mlTasks: unique(data.map((d) => d.machine_learning_task)), + agTasks: unique(data.map((d) => d.agricultural_task)), + environments: unique(data.map((d) => d.environment)), + augmentedCounterparts: unique( + data.map((d) => d.augmented_counterpart), + ), + cropTypes: unique(data.flatMap((d) => d.crop_types ?? [])), + locations: unique(data.map((d) => d.location)), + platforms: unique(data.flatMap((d) => d.platform ?? [])), + realOptions: unique(data.map((d) => d.real_or_synthetic)), + datasetTypes: unique(data.map((d) => d.dataset_type)), + qaTypes: unique(data.flatMap((d) => d.qa_type ?? [])), + taskDimensions: unique( + data.flatMap((d) => d.task_dimensions ?? []), + ), + }; + }, [data]); } export function toTitleCase(value: string): string { - return toDisplayLabel(value); + return toDisplayLabel(value); } function parsePlatform(raw: unknown): string[] | null { - const tokens: string[] = []; - const pushSplit = (s: string) => s.split(',').forEach((part) => { const t = part.trim(); if (t) tokens.push(t); }); - if (Array.isArray(raw)) { - raw.forEach((entry) => { if (typeof entry === 'string') pushSplit(entry); }); - } else if (typeof raw === 'string') { - pushSplit(raw); - } - if (!tokens.length) return null; - const seen = new Map(); - for (const t of tokens) { - const key = t.toLowerCase(); - if (!seen.has(key)) seen.set(key, toTitleCase(t)); - } - return Array.from(seen.values()); + const tokens: string[] = []; + const pushSplit = (s: string) => + s.split(",").forEach((part) => { + const t = part.trim(); + if (t) tokens.push(t); + }); + if (Array.isArray(raw)) { + raw.forEach((entry) => { + if (typeof entry === "string") pushSplit(entry); + }); + } else if (typeof raw === "string") { + pushSplit(raw); + } + if (!tokens.length) return null; + const seen = new Map(); + for (const t of tokens) { + const key = t.toLowerCase(); + if (!seen.has(key)) seen.set(key, toTitleCase(t)); + } + return Array.from(seen.values()); } -export function formatDisplayLocation(value: string | string[] | null | undefined): string { - if (value == null) return '—'; - return Array.isArray(value) ? value.map(toTitleCase).join(', ') : toTitleCase(value); +export function formatDisplayLocation( + value: string | string[] | null | undefined, +): string { + if (value == null) return "—"; + return Array.isArray(value) + ? value.map(toTitleCase).join(", ") + : toTitleCase(value); } export interface DatasetStats { - datasetCount: number; - imageCount: number; - taskTypeCount: number; + datasetCount: number; + imageCount: number; + taskTypeCount: number; } // Shared by the homepage stats row and the dataset search page's hero stats, so both // report the same real numbers instead of two slightly different ad-hoc counts. export function computeDatasetStats(datasets: Dataset[]): DatasetStats { - const topLevel = datasets.filter((dataset) => !dataset.parent_dataset); - const imageCount = topLevel - .filter((dataset) => !dataset.name.startsWith('iNatAg-mini')) - .reduce((sum, dataset) => sum + (dataset.num_images ?? 0), 0); - const taskTypeCount = new Set( - datasets.map((dataset) => dataset.machine_learning_task).filter((task): task is string => Boolean(task)) - ).size; - - return { - datasetCount: topLevel.length, - imageCount, - taskTypeCount, - }; + const topLevel = datasets.filter((dataset) => !dataset.parent_dataset); + const imageCount = topLevel + .filter((dataset) => !dataset.name.startsWith("iNatAg-mini")) + .reduce((sum, dataset) => sum + (dataset.num_images ?? 0), 0); + const taskTypeCount = new Set( + datasets + .map((dataset) => dataset.machine_learning_task) + .filter((task): task is string => Boolean(task)), + ).size; + + return { + datasetCount: topLevel.length, + imageCount, + taskTypeCount, + }; } \ No newline at end of file diff --git a/static/data/benchmarking-result/watermelon_disease_classification.embeddings.json b/static/data/benchmarking-result/watermelon_disease_classification.embeddings.json new file mode 100644 index 0000000..fce11ff --- /dev/null +++ b/static/data/benchmarking-result/watermelon_disease_classification.embeddings.json @@ -0,0 +1,608 @@ +[ + { + "x": -0.5371, + "y": 0.4798, + "z": 0.3412, + "class": "Anthracnose" + }, + { + "x": -0.5837, + "y": 0.4609, + "z": 0.3466, + "class": "Anthracnose" + }, + { + "x": -0.6294, + "y": 0.3709, + "z": 0.1457, + "class": "Anthracnose" + }, + { + "x": -0.6249, + "y": 0.5005, + "z": 0.3123, + "class": "Anthracnose" + }, + { + "x": -0.7338, + "y": 0.4981, + "z": 0.2611, + "class": "Anthracnose" + }, + { + "x": -0.5889, + "y": 0.3672, + "z": 0.3835, + "class": "Anthracnose" + }, + { + "x": -0.5281, + "y": 0.5017, + "z": 0.1415, + "class": "Anthracnose" + }, + { + "x": -0.5228, + "y": 0.5363, + "z": 0.2475, + "class": "Anthracnose" + }, + { + "x": -0.6058, + "y": 0.6525, + "z": 0.2764, + "class": "Anthracnose" + }, + { + "x": -0.5767, + "y": 0.5138, + "z": 0.4689, + "class": "Anthracnose" + }, + { + "x": -0.6355, + "y": 0.4391, + "z": 0.3445, + "class": "Anthracnose" + }, + { + "x": -0.6114, + "y": 0.5564, + "z": 0.2743, + "class": "Anthracnose" + }, + { + "x": -0.6542, + "y": 0.5349, + "z": 0.3018, + "class": "Anthracnose" + }, + { + "x": -0.6212, + "y": 0.4816, + "z": 0.3772, + "class": "Anthracnose" + }, + { + "x": 0.5006, + "y": 0.5414, + "z": -0.5324, + "class": "Downy_Mildew" + }, + { + "x": 0.4931, + "y": 0.5999, + "z": -0.5056, + "class": "Downy_Mildew" + }, + { + "x": 0.4939, + "y": 0.5324, + "z": -0.3516, + "class": "Downy_Mildew" + }, + { + "x": 0.6047, + "y": 0.4079, + "z": -0.4642, + "class": "Downy_Mildew" + }, + { + "x": 0.4786, + "y": 0.3239, + "z": -0.4518, + "class": "Downy_Mildew" + }, + { + "x": 0.662, + "y": 0.529, + "z": -0.2372, + "class": "Downy_Mildew" + }, + { + "x": 0.548, + "y": 0.5799, + "z": -0.3068, + "class": "Downy_Mildew" + }, + { + "x": 0.7042, + "y": 0.4094, + "z": -0.4327, + "class": "Downy_Mildew" + }, + { + "x": 0.7871, + "y": 0.7323, + "z": -0.4228, + "class": "Downy_Mildew" + }, + { + "x": 0.6407, + "y": 0.6372, + "z": -0.4306, + "class": "Downy_Mildew" + }, + { + "x": 0.3842, + "y": 0.7857, + "z": -0.4402, + "class": "Downy_Mildew" + }, + { + "x": 0.3192, + "y": 0.5058, + "z": -0.2236, + "class": "Downy_Mildew" + }, + { + "x": 0.5234, + "y": 0.5215, + "z": -0.4966, + "class": "Downy_Mildew" + }, + { + "x": 0.4212, + "y": 0.6001, + "z": -0.6051, + "class": "Downy_Mildew" + }, + { + "x": 0.4747, + "y": 0.6174, + "z": -0.516, + "class": "Downy_Mildew" + }, + { + "x": 0.5063, + "y": 0.5778, + "z": -0.2429, + "class": "Downy_Mildew" + }, + { + "x": 0.6345, + "y": 0.2949, + "z": -0.5122, + "class": "Downy_Mildew" + }, + { + "x": 0.7067, + "y": 0.385, + "z": -0.3658, + "class": "Downy_Mildew" + }, + { + "x": 0.6835, + "y": 0.5821, + "z": -0.2471, + "class": "Downy_Mildew" + }, + { + "x": 0.5757, + "y": 0.6581, + "z": -0.2741, + "class": "Downy_Mildew" + }, + { + "x": 0.6742, + "y": 0.6974, + "z": -0.3819, + "class": "Downy_Mildew" + }, + { + "x": 0.5795, + "y": 0.6582, + "z": -0.3534, + "class": "Downy_Mildew" + }, + { + "x": 0.6021, + "y": 0.5892, + "z": -0.3691, + "class": "Downy_Mildew" + }, + { + "x": 0.7262, + "y": 0.4487, + "z": -0.2913, + "class": "Downy_Mildew" + }, + { + "x": 0.6112, + "y": 0.3788, + "z": -0.6011, + "class": "Downy_Mildew" + }, + { + "x": 0.7385, + "y": 0.3032, + "z": -0.3277, + "class": "Downy_Mildew" + }, + { + "x": 0.7549, + "y": 0.455, + "z": -0.5113, + "class": "Downy_Mildew" + }, + { + "x": 0.5499, + "y": 0.481, + "z": -0.2025, + "class": "Downy_Mildew" + }, + { + "x": 0.2634, + "y": 0.397, + "z": -0.1977, + "class": "Downy_Mildew" + }, + { + "x": 0.4441, + "y": 0.6408, + "z": -0.4011, + "class": "Downy_Mildew" + }, + { + "x": 0.4375, + "y": 0.6819, + "z": -0.3732, + "class": "Downy_Mildew" + }, + { + "x": 0.5216, + "y": 0.4874, + "z": -0.4227, + "class": "Downy_Mildew" + }, + { + "x": 0.5906, + "y": 0.5093, + "z": -0.3286, + "class": "Downy_Mildew" + }, + { + "x": -0.5651, + "y": -0.4911, + "z": -0.2344, + "class": "Healthy" + }, + { + "x": -0.4924, + "y": -0.5942, + "z": -0.2484, + "class": "Healthy" + }, + { + "x": -0.4563, + "y": -0.4767, + "z": -0.2676, + "class": "Healthy" + }, + { + "x": -0.4696, + "y": -0.6621, + "z": -0.1762, + "class": "Healthy" + }, + { + "x": -0.6105, + "y": -0.5293, + "z": -0.0852, + "class": "Healthy" + }, + { + "x": -0.5488, + "y": -0.5548, + "z": -0.2716, + "class": "Healthy" + }, + { + "x": -0.5152, + "y": -0.5849, + "z": -0.1485, + "class": "Healthy" + }, + { + "x": -0.5141, + "y": -0.5493, + "z": -0.2246, + "class": "Healthy" + }, + { + "x": -0.3814, + "y": -0.5782, + "z": -0.2018, + "class": "Healthy" + }, + { + "x": -0.4648, + "y": -0.4516, + "z": 0.0247, + "class": "Healthy" + }, + { + "x": -0.3303, + "y": -0.5906, + "z": -0.2157, + "class": "Healthy" + }, + { + "x": -0.4556, + "y": -0.4373, + "z": -0.2687, + "class": "Healthy" + }, + { + "x": -0.555, + "y": -0.6288, + "z": -0.124, + "class": "Healthy" + }, + { + "x": -0.4307, + "y": -0.5382, + "z": -0.1613, + "class": "Healthy" + }, + { + "x": -0.4701, + "y": -0.564, + "z": -0.1689, + "class": "Healthy" + }, + { + "x": -0.5293, + "y": -0.4989, + "z": -0.1504, + "class": "Healthy" + }, + { + "x": -0.5272, + "y": -0.4786, + "z": -0.11, + "class": "Healthy" + }, + { + "x": -0.6049, + "y": -0.6277, + "z": -0.3388, + "class": "Healthy" + }, + { + "x": 0.5447, + "y": -0.4641, + "z": 0.4934, + "class": "Mosaic_Virus" + }, + { + "x": 0.4913, + "y": -0.3394, + "z": 0.4916, + "class": "Mosaic_Virus" + }, + { + "x": 0.3852, + "y": -0.2969, + "z": 0.6683, + "class": "Mosaic_Virus" + }, + { + "x": 0.8242, + "y": -0.3016, + "z": 0.8374, + "class": "Mosaic_Virus" + }, + { + "x": 0.2307, + "y": -0.0526, + "z": 0.3463, + "class": "Mosaic_Virus" + }, + { + "x": 0.9259, + "y": -0.5112, + "z": 0.2733, + "class": "Mosaic_Virus" + }, + { + "x": 0.7666, + "y": -0.6434, + "z": 0.3074, + "class": "Mosaic_Virus" + }, + { + "x": 0.8781, + "y": -0.1149, + "z": 0.4637, + "class": "Mosaic_Virus" + }, + { + "x": 0.7735, + "y": -0.9234, + "z": 0.4123, + "class": "Mosaic_Virus" + }, + { + "x": 0.2721, + "y": -0.8323, + "z": 0.4707, + "class": "Mosaic_Virus" + }, + { + "x": 0.4212, + "y": -0.5899, + "z": 0.3888, + "class": "Mosaic_Virus" + }, + { + "x": 0.5674, + "y": -0.1395, + "z": 0.4257, + "class": "Mosaic_Virus" + }, + { + "x": 0.8773, + "y": -0.5277, + "z": 0.1169, + "class": "Mosaic_Virus" + }, + { + "x": 0.5939, + "y": -0.2231, + "z": 0.5545, + "class": "Mosaic_Virus" + }, + { + "x": 0.7731, + "y": -0.4845, + "z": 0.5336, + "class": "Mosaic_Virus" + }, + { + "x": 0.3831, + "y": -0.0835, + "z": 0.3668, + "class": "Mosaic_Virus" + }, + { + "x": 0.7472, + "y": -0.4409, + "z": 0.0445, + "class": "Mosaic_Virus" + }, + { + "x": 0.1401, + "y": -0.3867, + "z": 0.7293, + "class": "Mosaic_Virus" + }, + { + "x": 0.6169, + "y": -0.6671, + "z": 0.3735, + "class": "Mosaic_Virus" + }, + { + "x": 0.419, + "y": -0.5497, + "z": 0.536, + "class": "Mosaic_Virus" + }, + { + "x": 0.8479, + "y": -0.7832, + "z": 0.1392, + "class": "Mosaic_Virus" + }, + { + "x": 0.3211, + "y": -0.2877, + "z": 0.2197, + "class": "Mosaic_Virus" + }, + { + "x": 1.1199, + "y": -0.3424, + "z": 0.47, + "class": "Mosaic_Virus" + }, + { + "x": 0.7166, + "y": -0.5956, + "z": 0.5132, + "class": "Mosaic_Virus" + }, + { + "x": 0.5344, + "y": -0.5017, + "z": 0.4961, + "class": "Mosaic_Virus" + }, + { + "x": 0.2388, + "y": -0.0598, + "z": 0.2411, + "class": "Mosaic_Virus" + }, + { + "x": 0.5085, + "y": 0.043, + "z": 0.1206, + "class": "Mosaic_Virus" + }, + { + "x": 0.4844, + "y": -0.3381, + "z": 0.5108, + "class": "Mosaic_Virus" + }, + { + "x": 0.7622, + "y": -0.8468, + "z": 0.3891, + "class": "Mosaic_Virus" + }, + { + "x": 0.7271, + "y": -0.6466, + "z": 0.3266, + "class": "Mosaic_Virus" + }, + { + "x": 0.7951, + "y": -0.1668, + "z": 0.3962, + "class": "Mosaic_Virus" + }, + { + "x": 0.4832, + "y": -0.1133, + "z": 0.6479, + "class": "Mosaic_Virus" + }, + { + "x": 0.6617, + "y": -0.4372, + "z": 0.6988, + "class": "Mosaic_Virus" + }, + { + "x": 0.7166, + "y": -0.7206, + "z": 0.1462, + "class": "Mosaic_Virus" + }, + { + "x": 0.319, + "y": -0.6888, + "z": 0.3908, + "class": "Mosaic_Virus" + }, + { + "x": 0.568, + "y": -0.4193, + "z": 0.8111, + "class": "Mosaic_Virus" + } +] diff --git a/static/data/benchmarking-result/watermelon_disease_classification.json b/static/data/benchmarking-result/watermelon_disease_classification.json new file mode 100644 index 0000000..7ad1a79 --- /dev/null +++ b/static/data/benchmarking-result/watermelon_disease_classification.json @@ -0,0 +1,86 @@ +{ + "dataset": "Project-AgML/watermelon_disease_classification", + "run_id": "20260720_185749", + "phases_completed": [1, 2], + "metrics": { + "class_imbalance": { + "counts": { + "Anthracnose": 109, + "Downy_Mildew": 266, + "Healthy": 143, + "Mosaic_Virus": 290 + }, + "imbalance_ratio": 2.6606, + "normalized_entropy": 0.9452, + "most_frequent_class": "Mosaic_Virus", + "least_frequent_class": "Anthracnose", + "total_train_examples": 808 + }, + "exact_duplicate": { + "total_images": 1155, + "exact_duplicate_count": 0, + "exact_duplicate_rate": 0.0, + "duplicate_groups": 0, + "cross_split_duplicates": 0 + }, + "resolution_consistency": { + "total_images": 1155, + "width": { "mean": 3024, "std": 0.0, "min": 3024, "max": 3024 }, + "height": { + "mean": 3091.2, + "std": 251.55, + "min": 3024, + "max": 4032 + }, + "aspect_ratio": { + "mean": 0.98, + "std": 0.06, + "min": 0.75, + "max": 1.0 + }, + "area_cv": 0.0814, + "mode_distribution": { "RGB": 1155 } + }, + "metadata_coverage": { + "skipped": true, + "reason": "No metadata columns detected in dataset schema." + }, + "near_duplicate": { + "total_images": 1155, + "embed_model": "facebook/dinov2-base", + "threshold": 0.98, + "near_duplicate_count": 46, + "near_duplicate_rate": 0.0398, + "near_duplicate_groups": 22, + "cross_split_near_duplicates": 27, + "faiss_index_type": "IndexFlatIP" + }, + "feature_separability": { + "embed_model": "facebook/dinov2-base", + "n_total": 1155, + "n_silhouette_samples": 1155, + "silhouette_score": 0.2629, + "silhouette_interpretation": "moderate separation", + "davies_bouldin_index": 1.9661, + "davies_bouldin_interpretation": "some overlap between clusters", + "per_class_silhouette": { + "Anthracnose": 0.6231, + "Downy_Mildew": 0.4447, + "Healthy": 0.6217, + "Mosaic_Virus": -0.2152 + } + }, + "intra_class_diversity": { + "embed_model": "facebook/dinov2-base", + "per_class_diversity": { + "Anthracnose": 0.5802, + "Downy_Mildew": 0.3971, + "Healthy": 0.3354, + "Mosaic_Virus": 0.5747 + }, + "mean_diversity": 0.4718, + "min_diversity_class": "Healthy", + "max_diversity_class": "Anthracnose" + } + } +} From 33761e3457954192abf8deccfa066d0054331ea8 Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Sat, 1 Aug 2026 10:24:19 -0600 Subject: [PATCH 04/27] Re-added changes for Original Dataset button --- src/components/DatasetMetadataModal.tsx | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index c9f688e..774e569 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -96,6 +96,12 @@ function formatMetricScore(entry: { metrics: { key: string; label: string; value function formatLoaderInstructions(dataset: Dataset) { if (dataset.source === 'huggingface') { + if(dataset.dataset_type === 'vlm') { + return { + title: 'Load from Hugging Face', + code: `from datasets import load_dataset\nloader = load_dataset("Project-AgML/${dataset.name}")`, + }; + } return { title: 'Load from Hugging Face', code: `from agml.data.hf_loader import HuggingFaceDataLoader\nloader = HuggingFaceDataLoader("Project-AgML/${dataset.name}")`, @@ -748,14 +754,6 @@ export function DatasetMetadataModal({ ))} - {isVlm && dataset.parent_dataset && ( - - )} - {isVlm && dataset.qa_type && dataset.qa_type.length > 0 && (

QA Types

@@ -874,6 +872,11 @@ export function DatasetMetadataModal({ View on Hugging Face )} + {isVlm && dataset.parent_dataset && ( + + View Original Dataset + + )} )} From f8ab71cad6032fee627d7c7dd1f91240a4657ccc Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Mon, 3 Aug 2026 13:25:07 -0600 Subject: [PATCH 05/27] 1. Added chnages related to Embedding visualization as go.Scatter() was laggier due to each point being a DOM element. Switched to scattergl for performance and smoothness. 2. Added metric explanation tooltip for interpretation --- package-lock.json | 2751 +++++- package.json | 4 +- .../DatasetMetadataModal.module.css | 116 +- src/components/DatasetMetadataModal.tsx | 238 +- src/components/EmbeddingPlot2D.tsx | 145 + src/components/EmbeddingPlot3D.tsx | 152 + src/lib/benchmarks.ts | 68 +- src/lib/plotlyChrome.ts | 85 + ...lon_disease_classification.embeddings.json | 608 -- .../watermelon_disease_classification.json | 86 - ...rmelon_disease_classification_umap_2d.json | 8087 +++++++++++++++++ ...rmelon_disease_classification_umap_3d.json | 1 + static/data/dataset-benchmarking/results.json | 94 + 13 files changed, 11502 insertions(+), 933 deletions(-) create mode 100644 src/components/EmbeddingPlot2D.tsx create mode 100644 src/components/EmbeddingPlot3D.tsx create mode 100644 src/lib/plotlyChrome.ts delete mode 100644 static/data/benchmarking-result/watermelon_disease_classification.embeddings.json delete mode 100644 static/data/benchmarking-result/watermelon_disease_classification.json create mode 100644 static/data/dataset-benchmarking/embeddings/watermelon_disease_classification_umap_2d.json create mode 100644 static/data/dataset-benchmarking/embeddings/watermelon_disease_classification_umap_3d.json create mode 100644 static/data/dataset-benchmarking/results.json diff --git a/package-lock.json b/package-lock.json index 4f4ece8..c0cd4cc 100644 --- a/package-lock.json +++ b/package-lock.json @@ -13,9 +13,11 @@ "@docusaurus/preset-classic": "3.10.1", "@mdx-js/react": "^3.0.0", "clsx": "^2.0.0", + "plotly.js-dist-min": "^3.7.0", "prism-react-renderer": "^2.3.0", "react": "^19.0.0", - "react-dom": "^19.0.0" + "react-dom": "^19.0.0", + "react-plotly.js": "^4.1.0" }, "devDependencies": { "@docusaurus/module-type-aliases": "3.10.1", @@ -1979,6 +1981,26 @@ "node": ">=6.9.0" } }, + "node_modules/@choojs/findup": { + "version": "0.2.1", + "resolved": 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"3.0.3", "resolved": "https://registry.npmjs.org/write-file-atomic/-/write-file-atomic-3.0.3.tgz", @@ -19417,6 +22046,16 @@ "xml-js": "bin/cli.js" } }, + "node_modules/xtend": { + "version": "4.0.2", + "resolved": "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz", + "integrity": "sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==", + "license": "MIT", + "peer": true, + "engines": { + "node": ">=0.4" + } + }, "node_modules/yallist": { "version": "3.1.1", "resolved": "https://registry.npmjs.org/yallist/-/yallist-3.1.1.tgz", diff --git a/package.json b/package.json index 044d80b..ef22522 100644 --- a/package.json +++ b/package.json @@ -23,9 +23,11 @@ "@docusaurus/preset-classic": "3.10.1", "@mdx-js/react": "^3.0.0", "clsx": "^2.0.0", + "plotly.js-dist-min": "^3.7.0", "prism-react-renderer": "^2.3.0", "react": "^19.0.0", - "react-dom": "^19.0.0" + "react-dom": "^19.0.0", + "react-plotly.js": "^4.1.0" }, "devDependencies": { "@docusaurus/module-type-aliases": "3.10.1", diff --git a/src/components/DatasetMetadataModal.module.css b/src/components/DatasetMetadataModal.module.css index 897dd02..fb4489b 100644 --- a/src/components/DatasetMetadataModal.module.css +++ b/src/components/DatasetMetadataModal.module.css @@ -635,6 +635,72 @@ color: var(--agml-muted); } +.metricCardTitleRow { + display: flex; + align-items: center; + gap: 0.35rem; + min-width: 0; +} + +.infoTooltip { + position: relative; + display: inline-flex; + align-items: center; + justify-content: center; + width: 14px; + height: 14px; + flex-shrink: 0; + border-radius: 50%; + border: 1px solid var(--agml-border-strong); + color: var(--agml-muted); + font-family: var(--ifm-code-font-family); + font-size: 0.58rem; + font-weight: 700; + cursor: help; +} + +.infoTooltip:hover, +.infoTooltip:focus-visible { + border-color: var(--ifm-color-primary); + color: var(--ifm-color-primary); + outline: none; +} + +.infoTooltipIcon { + pointer-events: none; +} + +.infoTooltipBubble { + position: absolute; + bottom: calc(100% + 8px); + left: 0; + width: max-content; + max-width: 240px; + padding: 0.5rem 0.65rem; + border-radius: 6px; + border: 1px solid var(--agml-border-strong); + background: var(--agml-tag-bg); + color: var(--agml-text); + font-size: 0.72rem; + font-weight: 400; + line-height: 1.45; + text-transform: none; + letter-spacing: normal; + white-space: normal; + opacity: 0; + visibility: hidden; + pointer-events: none; + transition: opacity 0.12s ease; + box-shadow: var(--agml-shadow-strong); + z-index: 20; +} + +.infoTooltip:hover .infoTooltipBubble, +.infoTooltip:focus-visible .infoTooltipBubble { + opacity: 1; + visibility: visible; +} + .metricCardBadge { font-family: var(--ifm-code-font-family); font-size: 0.68rem; @@ -809,46 +875,42 @@ .embedViewport { position: relative; margin-top: 0.9rem; - height: 340px; + height: 440px; border-radius: 10px; border: 1px solid var(--agml-border); background-color: var(--agml-surface-soft); - background-image: linear-gradient(var(--agml-border) 1px, transparent 1px), - linear-gradient(90deg, var(--agml-border) 1px, transparent 1px); - background-size: 34px 34px; - background-position: center; overflow: hidden; } -.embedAxisTickX { - position: absolute; - bottom: 4px; - transform: translateX(-50%); - font-family: var(--ifm-code-font-family); - font-size: 0.62rem; +.embedPlotFallback { + display: flex; + align-items: center; + justify-content: center; + height: 100%; color: var(--agml-muted); - pointer-events: none; + font-size: 0.78rem; } -.embedAxisTickY { +.embedResetButton { position: absolute; - left: 4px; - transform: translateY(-50%); - font-family: var(--ifm-code-font-family); - font-size: 0.62rem; + top: 10px; + right: 10px; + z-index: 5; + border: 1px solid var(--agml-border-strong); + border-radius: 999px; + background: var(--agml-tag-bg); color: var(--agml-muted); - pointer-events: none; + font-family: var(--ifm-code-font-family); + font-size: 0.68rem; + padding: 0.3rem 0.65rem; + cursor: pointer; } -.embedDragHint { - position: absolute; - left: 14px; - bottom: 12px; - margin: 0; - font-size: 0.7rem; - color: var(--agml-muted); - font-family: var(--ifm-code-font-family); - pointer-events: none; +.embedResetButton:hover, +.embedResetButton:focus-visible { + border-color: var(--ifm-color-primary); + color: var(--ifm-color-primary); + outline: none; } .embedLegend { diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index 774e569..d1de8e5 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -1,11 +1,12 @@ -import { Fragment, useEffect, useMemo, useRef, useState } from 'react'; -import type { MouseEvent as ReactMouseEvent } from 'react'; +import { Fragment, useEffect, useMemo, useState } from 'react'; import type { Dataset } from '../lib/datasets'; import { formatDisplayLocation, toTitleCase } from '../lib/datasets'; import { toDisplayLabel } from '../lib/labelOverrides'; import { useBenchmark, type BenchmarkData, type EmbedPoint as RawEmbedPoint } from '../lib/benchmarks'; import { METRIC_CATEGORY_LABELS, useDatasetPerformance } from '../lib/performance'; import type { MetricCategory, PerformanceEntry } from '../lib/performance'; +import { EmbeddingPlot2D } from './EmbeddingPlot2D'; +import { EmbeddingPlot3D } from './EmbeddingPlot3D'; import styles from './DatasetMetadataModal.module.css'; function formatImageCount(count: number | null) { @@ -126,9 +127,9 @@ function StatTile({ label, value }: { label: string; value: string }) { type MetricBar = { label: string; value: string; pct: number; positive?: boolean }; type MetricStat = { label: string; value: string }; type MetricCardVM = - | { kind: 'bars'; title: string; badge?: string; bars: MetricBar[]; footerStats?: MetricStat[] } - | { kind: 'signed'; title: string; badge?: string; bars: MetricBar[]; footerStats?: MetricStat[] } - | { kind: 'stats'; title: string; badge?: string; stats: MetricStat[] } + | { kind: 'bars'; title: string; description: string; badge?: string; bars: MetricBar[]; footerStats?: MetricStat[] } + | { kind: 'signed'; title: string; description: string; badge?: string; bars: MetricBar[]; footerStats?: MetricStat[] } + | { kind: 'stats'; title: string; description: string; badge?: string; stats: MetricStat[] } | { kind: 'skipped'; title: string; message: string }; function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { @@ -141,6 +142,8 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { cards.push({ kind: 'bars', title: 'Class Imbalance', + description: + 'How evenly the classes are represented. Imbalance ratio is the most-frequent class’s count divided by the least-frequent’s (1.0 = perfectly balanced, higher = more skewed). Normalized entropy ranges 0–1, where 1 means every class has an equal share.', badge: `${d.total_train_examples} train examples`, bars: Object.entries(d.counts).map(([label, value]) => ({ label: toTitleCase(label), value: value.toLocaleString(), pct: (value / max) * 100 })), footerStats: [ @@ -157,6 +160,8 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { cards.push({ kind: 'stats', title: 'Exact Duplicates', + description: + 'Images that are byte-for-byte identical to another image in the dataset. A high duplicate rate inflates the apparent dataset size and can leak the same example across train/test splits, making evaluation look better than it is.', badge: `${d.total_images} images`, stats: [ { label: 'Duplicate count', value: String(d.exact_duplicate_count) }, @@ -172,6 +177,8 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { cards.push({ kind: 'stats', title: 'Near Duplicates', + description: + 'Images that are nearly identical (crops, recompressions, small edits) based on embedding similarity above a threshold — not exact byte matches. High near-duplicate rates risk train/test leakage even when the exact-duplicate count is zero.', badge: d.embed_model, stats: [ { label: 'Near-dup count', value: String(d.near_duplicate_count) }, @@ -189,6 +196,8 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { cards.push({ kind: 'stats', title: 'Resolution Consistency', + description: + 'How uniform image dimensions and aspect ratios are across the dataset. Area CV (coefficient of variation) near 0 means sizes barely vary; higher values mean preprocessing has to handle a wide range of source resolutions.', badge: `${d.total_images} images`, stats: [ { label: 'Width (mean)', value: `${d.width.mean}px` }, @@ -205,6 +214,8 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { cards.push({ kind: 'signed', title: 'Feature Separability', + description: + 'How distinctly the classes cluster in embedding space. Silhouette score ranges −1 to 1 (higher = better-separated clusters, near 0 = overlapping, negative = points closer to another class than their own — often mislabeled or genuinely ambiguous). Davies-Bouldin index is ≥0 and unbounded, where lower means better separation.', badge: d.embed_model, bars: Object.entries(d.per_class_silhouette).map(([label, value]) => ({ label: toTitleCase(label), @@ -225,6 +236,8 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { cards.push({ kind: 'bars', title: 'Intra-class Diversity', + description: + 'How visually varied the images within each class are, based on average embedding distance between same-class examples. Higher values mean more diverse, less redundant examples per class; a very low value can mean that class is dominated by near-duplicates.', badge: d.embed_model, bars: Object.entries(d.per_class_diversity).map(([label, value]) => ({ label: toTitleCase(label), value: value.toFixed(2), pct: (value / max) * 100 })), footerStats: [ @@ -242,12 +255,26 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { return cards; } +function InfoTooltip({ text }: { text: string }) { + return ( + + + {text} + + ); +} + function MetricCard({ card }: { card: MetricCardVM }) { const badge = card.kind !== 'skipped' ? card.badge : undefined; return (
-

{card.title}

+
+

{card.title}

+ {card.kind !== 'skipped' && } +
{badge && {badge}}
@@ -300,8 +327,7 @@ function MetricCard({ card }: { card: MetricCardVM }) { ); } -const EMBED_CLASS_COLORS = ['var(--ifm-color-primary)', 'oklch(0.66 0.15 60)', 'oklch(0.58 0.14 250)', 'oklch(0.62 0.16 340)']; -const AXIS_TICKS = [-1, -0.5, 0, 0.5, 1]; +const PALETTE = ['#4C9BE8', '#F4A261', '#2EC4B6', '#E63946', '#A8DADC', '#8ECAE6', '#FFB4A2', '#B5838D']; const EMBED_CLUSTER_CENTERS: [number, number, number][] = [ [-0.6, 0.5, 0.3], [0.55, 0.55, -0.4], @@ -319,21 +345,25 @@ function mulberry32(seed: number) { }; } -type EmbedPoint = { id: string; cls: string; color: string; x: number; y: number; z: number }; - -function colorForClass(cls: string, classOrder: string[]): string { - const index = classOrder.indexOf(cls); - return EMBED_CLASS_COLORS[(index < 0 ? 0 : index) % EMBED_CLASS_COLORS.length]; +export interface EmbedPoint { + id: string; + cls: string; + split: string; + index: number; + x: number; + y: number; + z: number; } -function embedPointsFromReal(embeddings: RawEmbedPoint[], classOrder: string[]): EmbedPoint[] { +function embedPointsFromReal(embeddings: RawEmbedPoint[]): EmbedPoint[] { return embeddings.map((p, i) => ({ - id: `${p.class}-${i}`, - cls: p.class, - color: colorForClass(p.class, classOrder), + id: `${p.label}-${p.index ?? i}`, + cls: p.label, + split: p.split ?? 'train', + index: p.index ?? i, x: p.x, y: p.y, - z: p.z, + z: p.z ?? 0, })); } @@ -342,96 +372,57 @@ function buildEmbeddingPoints(benchmark: BenchmarkData): EmbedPoint[] { const sep = benchmark.metrics.feature_separability?.per_class_silhouette ?? {}; const classes = Object.keys(counts); const rand = mulberry32(42); + const splits = ['train', 'val', 'test']; const points: EmbedPoint[] = []; + let globalIndex = 0; classes.forEach((cls, i) => { const center = EMBED_CLUSTER_CENTERS[i % EMBED_CLUSTER_CENTERS.length]; - const color = EMBED_CLASS_COLORS[i % EMBED_CLASS_COLORS.length]; const sil = sep[cls] ?? 0.3; const spread = 0.22 + Math.max(0, 0.65 - sil) * 0.55; const n = Math.max(6, Math.round((counts[cls] || 30) / 8)); for (let k = 0; k < n; k += 1) { const jitter = () => (((rand() + rand() + rand()) - 1.5) / 1.5) * spread; - points.push({ id: `${cls}-${k}`, cls, color, x: center[0] + jitter(), y: center[1] + jitter(), z: center[2] + jitter() }); + points.push({ + id: `${cls}-${k}`, + cls, + split: splits[globalIndex % splits.length], + index: globalIndex, + x: center[0] + jitter(), + y: center[1] + jitter(), + z: center[2] + jitter(), + }); + globalIndex += 1; } }); return points; } -function projectEmbeddingPoints(points: EmbedPoint[], theta: number, phi: number, mode: '2d' | '3d') { - const focal = 2.6; - const projected = points.map((p) => { - if (mode === '2d') return { ...p, sx: p.x, sy: p.y, depth: 0, scale: 1 }; - const cosT = Math.cos(theta); - const sinT = Math.sin(theta); - const x1 = p.x * cosT + p.z * sinT; - const z1 = -p.x * sinT + p.z * cosT; - const cosP = Math.cos(phi); - const sinP = Math.sin(phi); - const y2 = p.y * cosP - z1 * sinP; - const z2 = p.y * sinP + z1 * cosP; - const scale = focal / (focal + z2); - return { ...p, sx: x1 * scale, sy: y2 * scale, depth: z2, scale }; - }); - if (mode !== '2d') projected.sort((a, b) => a.depth - b.depth); - return projected; +function buildClassColorMap(points: EmbedPoint[]): Record { + const labels = Array.from(new Set(points.map((p) => p.cls))).sort(); + return Object.fromEntries(labels.map((label, i) => [label, PALETTE[i % PALETTE.length]])); } -function EmbeddingScatter({ benchmark, embeddings }: { benchmark: BenchmarkData; embeddings: RawEmbedPoint[] | null }) { +function EmbeddingScatter({ + benchmark, + embeddings2d, + embeddings3d, +}: { + benchmark: BenchmarkData; + embeddings2d: RawEmbedPoint[] | null; + embeddings3d: RawEmbedPoint[] | null; +}) { const [view, setView] = useState<'2d' | '3d'>('2d'); - const [theta, setTheta] = useState(0.6); - const [phi, setPhi] = useState(0.32); - const draggingRef = useRef(false); - const lastPointerRef = useRef<{ x: number; y: number } | null>(null); - const hasRealEmbeddings = Boolean(embeddings && embeddings.length > 0); + // 2D and 3D UMAP projections are independently fit, so they're separate point sets — + // not the same points with one axis dropped. + const activeRaw = view === '2d' ? embeddings2d : embeddings3d; + const hasRealEmbeddings = Boolean(activeRaw && activeRaw.length > 0); const points = useMemo(() => { - if (embeddings && embeddings.length > 0) { - const classOrder = Object.keys(benchmark.metrics.class_imbalance?.counts ?? {}); - return embedPointsFromReal(embeddings, classOrder); - } + if (activeRaw && activeRaw.length > 0) return embedPointsFromReal(activeRaw); return buildEmbeddingPoints(benchmark); - }, [embeddings, benchmark]); - - useEffect(() => { - const timer = setInterval(() => { - if (draggingRef.current) return; - setTheta((t) => t + 0.006); - }, 40); - return () => clearInterval(timer); - }, []); - - useEffect(() => { - const onMove = (event: MouseEvent) => { - if (!lastPointerRef.current) return; - const dx = event.clientX - lastPointerRef.current.x; - const dy = event.clientY - lastPointerRef.current.y; - lastPointerRef.current = { x: event.clientX, y: event.clientY }; - setTheta((t) => t + dx * 0.008); - setPhi((p) => Math.max(-1.2, Math.min(1.2, p - dy * 0.008))); - }; - const onUp = () => { - draggingRef.current = false; - lastPointerRef.current = null; - }; - window.addEventListener('mousemove', onMove); - window.addEventListener('mouseup', onUp); - return () => { - window.removeEventListener('mousemove', onMove); - window.removeEventListener('mouseup', onUp); - }; - }, []); - - const onDown = (event: ReactMouseEvent) => { - draggingRef.current = true; - lastPointerRef.current = { x: event.clientX, y: event.clientY }; - }; + }, [activeRaw, benchmark]); - const projected = useMemo(() => projectEmbeddingPoints(points, theta, phi, view), [points, theta, phi, view]); - const legend = useMemo(() => { - const seen = new Map(); - points.forEach((p) => seen.set(p.cls, p.color)); - return Array.from(seen.entries()); - }, [points]); + const colorMap = useMemo(() => buildClassColorMap(points), [points]); const embedModel = benchmark.metrics.feature_separability?.embed_model ?? 'dinov2-base'; @@ -460,75 +451,56 @@ function EmbeddingScatter({ benchmark, embeddings }: { benchmark: BenchmarkData;
-
- {AXIS_TICKS.map((value) => ( - - {value} - - ))} - {AXIS_TICKS.map((value) => ( - - {value} - - ))} - {projected.map((p) => ( -
- ))} - {view === '3d' &&

drag to orbit

} -
+ {view === '2d' ? ( + + ) : ( + + )}
- {legend.map(([cls, color]) => ( - + {Object.entries(colorMap).map(([key, color]) => ( + - {toTitleCase(cls)} + {toTitleCase(key)} ))}

{hasRealEmbeddings - ? 'projected from embeddings.json' - : `no embeddings.json found for this dataset · showing a seeded demo scatter around class clusters`} + ? `projected from real ${view.toUpperCase()} UMAP embeddings` + : `no ${view.toUpperCase()} UMAP embeddings found for this dataset · showing a seeded demo scatter around class clusters`}

); } -function formatRunDate(runId: string): string { - const match = /^(\d{4})(\d{2})(\d{2})/.exec(runId); - if (!match) return runId; +function formatBenchmarkDate(dateStr: string): string { + const match = /^(\d{4})-(\d{2})-(\d{2})/.exec(dateStr); + if (!match) return dateStr; const [, year, month, day] = match; return `${month}/${day}/${year}`; } -function BenchmarkView({ benchmark, embeddings }: { benchmark: BenchmarkData; embeddings: RawEmbedPoint[] | null }) { +function BenchmarkView({ + benchmark, + embeddings2d, + embeddings3d, +}: { + benchmark: BenchmarkData; + embeddings2d: RawEmbedPoint[] | null; + embeddings3d: RawEmbedPoint[] | null; +}) { const cards = useMemo(() => buildMetricCards(benchmark), [benchmark]); return (
-

Last benchmarking check run: {formatRunDate(benchmark.run_id)}

+

Last benchmarking check run: {formatBenchmarkDate(benchmark.date)}

{cards.map((card) => ( ))}
- +
); } @@ -593,7 +565,7 @@ export function DatasetMetadataModal({ }, [open, onClose]); const datasetPerformance = useDatasetPerformance(open ? (dataset?.name ?? null) : null); - const { data: benchmark, embeddings } = useBenchmark(open ? (dataset?.name ?? null) : null); + const { data: benchmark, embeddings2d, embeddings3d } = useBenchmark(open ? (dataset?.name ?? null) : null); const [showBenchmarks, setShowBenchmarks] = useState(false); useEffect(() => setShowBenchmarks(false), [dataset?.name, open]); @@ -1028,7 +1000,7 @@ export function DatasetMetadataModal({ className={`${styles.face} ${styles.faceBack} ${showBenchmarks ? '' : styles.faceHidden}`} style={{ visibility: showBenchmarks ? 'visible' : 'hidden', pointerEvents: showBenchmarks ? 'auto' : 'none' }} > - + )} diff --git a/src/components/EmbeddingPlot2D.tsx b/src/components/EmbeddingPlot2D.tsx new file mode 100644 index 0000000..4ea9299 --- /dev/null +++ b/src/components/EmbeddingPlot2D.tsx @@ -0,0 +1,145 @@ +import { useMemo, useRef } from 'react'; +import BrowserOnly from '@docusaurus/BrowserOnly'; +import { toTitleCase } from '../lib/datasets'; +import { usePlotlyChrome } from '../lib/plotlyChrome'; +import type { EmbedPoint } from './DatasetMetadataModal'; +import styles from './DatasetMetadataModal.module.css'; + +// scattergl (WebGL) instead of the SVG scatter trace — SVG puts one DOM node per point and +// bogs down well before real dataset sizes. See EmbeddingPlot3D for the SSR/bundle notes; +// both views share the same plotly.js-dist-min instance. +function buildTraces( + points: EmbedPoint[], + colorMap: Record, + hoverlabel: { bgcolor: string; bordercolor: string; font: { color: string } }, + markerRing: string, +) { + return Object.keys(colorMap).map((group) => { + const subset = points.filter((p) => p.cls === group); + return { + type: 'scattergl', + mode: 'markers', + name: toTitleCase(group), + x: subset.map((p) => p.x), + y: subset.map((p) => p.y), + // A thin ring in the panel's own surface color separates overlapping points instead + // of them just fusing into a blob at the edges. + marker: { size: 7, color: colorMap[group], opacity: 0.85, line: { width: 1, color: markerRing } }, + text: subset.map((p) => `${toTitleCase(p.cls)} · ${p.split} · #${p.index}`), + hovertemplate: '%{text}', + // Set per-trace, not just at the layout level — Plotly's hoverlabel.bgcolor + // default is "auto" (derived from the trace/marker color), which otherwise wins. + hoverlabel, + }; + }); +} + +export function EmbeddingPlot2D({ + points, + colorMap, +}: { + points: EmbedPoint[]; + colorMap: Record; +}) { + const chrome = usePlotlyChrome(); + const hoverlabel = useMemo( + () => ({ + bgcolor: chrome.tooltipBg, + bordercolor: chrome.tooltipBorder, + font: { color: chrome.tooltipText }, + }), + [chrome], + ); + const traces = useMemo( + () => buildTraces(points, colorMap, hoverlabel, chrome.markerRing), + [points, colorMap, hoverlabel, chrome.markerRing], + ); + + // Captured once the plot mounts, so the "Reset view" button can relayout it directly. + const plotlyRef = useRef(null); + const graphDivRef = useRef(null); + const handleResetView = () => { + if (!plotlyRef.current || !graphDivRef.current) return; + plotlyRef.current.relayout(graphDivRef.current, { 'xaxis.autorange': true, 'yaxis.autorange': true }); + }; + + // Recessive hairline grid with numeric ticks — gives the cloud a spatial reference frame + // instead of floating in a void. A *fresh* object per axis — Plotly's internal axis + // bookkeeping got confused when xaxis/yaxis pointed at the literal same object (identical + // autoranged x/y spans regardless of actual data, and scroll-zoom anchoring at the wrong + // point), so this is a factory, not a shared constant. + const makeAxis = () => ({ + showgrid: true, + gridcolor: chrome.grid, + zeroline: false, + showticklabels: true, + linecolor: chrome.grid, + tickfont: { color: chrome.text, family: chrome.fontFamily, size: 10 }, + }); + + return ( +
+ Loading 2D view…
}> + {() => { + const Plotly = require('plotly.js-dist-min'); + plotlyRef.current = Plotly; + const createPlotlyComponent = require('react-plotly.js/factory').default; + const Plot = createPlotlyComponent(Plotly); + return ( + { + graphDivRef.current = graphDiv; + }} + onUpdate={(_figure: unknown, graphDiv: HTMLElement) => { + graphDivRef.current = graphDiv; + }} + layout={{ + autosize: true, + margin: { l: 35, r: 15, t: 15, b: 30 }, + paper_bgcolor: 'transparent', + plot_bgcolor: 'transparent', + // x/y autorange independently (no scaleanchor) so the plot fills the panel's + // actual aspect ratio instead of leaving gutters to preserve a 1:1 scale. + xaxis: makeAxis(), + yaxis: makeAxis(), + showlegend: false, + hoverlabel: { + bgcolor: chrome.tooltipBg, + bordercolor: chrome.tooltipBorder, + font: { color: chrome.tooltipText }, + }, + // Drag disabled outright — Plotly's default 'zoom' draws a rubber-band select + // box, and 'pan' has a rendering-pipeline quirk where the SVG grid/tick layer + // only catches up to the WebGL point layer at drag-end instead of tracking it + // live, which reads as the grid "jumping" mid-drag. Scroll-zoom (below) has + // neither problem since each wheel tick is a discrete, fully-committed relayout. + dragmode: false, + // A constant uirevision tells Plotly to keep whatever zoom/pan the user set + // across re-renders. Without it, any re-render (e.g. a theme toggle recomputing + // chrome colors) passes a "fresh" layout with only autorange requested, and + // Plotly.react() reads that as "reset the view" — the zoom snapping back you saw. + uirevision: 'embedding-2d', + }} + // Plotly's cartesian plots default scrollZoom to false (only mapbox/geo get it + // for free) — this adds the scroll-wheel zoom people expect from a scatter plot. + config={{ displayModeBar: false, responsive: true, scrollZoom: true }} + style={{ width: '100%', height: '100%' }} + useResizeHandler + /> + ); + }} + + + + ); +} + +export default EmbeddingPlot2D; diff --git a/src/components/EmbeddingPlot3D.tsx b/src/components/EmbeddingPlot3D.tsx new file mode 100644 index 0000000..5566db1 --- /dev/null +++ b/src/components/EmbeddingPlot3D.tsx @@ -0,0 +1,152 @@ +import { useMemo, useRef } from 'react'; +import BrowserOnly from '@docusaurus/BrowserOnly'; +import { toTitleCase } from '../lib/datasets'; +import { usePlotlyChrome } from '../lib/plotlyChrome'; +import type { EmbedPoint } from './DatasetMetadataModal'; +import styles from './DatasetMetadataModal.module.css'; + +// Plotly's default eye distance (1.25 on each axis) leaves a lot of headroom around the +// cube — this pulls the camera in so it fills the panel. Shared with the reset handler below +// so "Reset view" restores exactly this framing, not Plotly's own (more zoomed-out) default. +const DEFAULT_CAMERA_EYE = { x: 0.85, y: 0.85, z: 0.85 }; + +// plotly.js touches `window` at import time, which breaks Docusaurus's build-time SSR pass — +// the require() calls for it must stay inside a children function so they're +// never evaluated outside the browser. This uses the full plotly.js-dist-min bundle (shared +// with EmbeddingPlot2D's scattergl trace) since 2D and 3D need different partial-bundle trace +// families (gl2d vs gl3d) that no single partial bundle covers together. +function buildTraces( + points: EmbedPoint[], + colorMap: Record, + hoverlabel: { bgcolor: string; bordercolor: string; font: { color: string } }, + markerRing: string, +) { + return Object.keys(colorMap).map((group) => { + const subset = points.filter((p) => p.cls === group); + return { + type: 'scatter3d', + mode: 'markers', + name: toTitleCase(group), + x: subset.map((p) => p.x), + y: subset.map((p) => p.y), + z: subset.map((p) => p.z), + // A thin ring in the panel's own surface color separates overlapping points instead + // of them just fusing into a blob at the edges. + marker: { size: 4, color: colorMap[group], opacity: 0.85, line: { width: 0.5, color: markerRing } }, + text: subset.map((p) => `${toTitleCase(p.cls)} · ${p.split} · #${p.index}`), + hovertemplate: '%{text}', + // Set per-trace, not just at the layout level — Plotly's hoverlabel.bgcolor + // default is "auto" (derived from the trace/marker color), which otherwise wins. + hoverlabel, + }; + }); +} + +export function EmbeddingPlot3D({ + points, + colorMap, +}: { + points: EmbedPoint[]; + colorMap: Record; +}) { + const chrome = usePlotlyChrome(); + const hoverlabel = useMemo( + () => ({ + bgcolor: chrome.tooltipBg, + bordercolor: chrome.tooltipBorder, + font: { color: chrome.tooltipText }, + }), + [chrome], + ); + const traces = useMemo( + () => buildTraces(points, colorMap, hoverlabel, chrome.markerRing), + [points, colorMap, hoverlabel, chrome.markerRing], + ); + + // Captured once the plot mounts, so the "Reset view" button can relayout it directly. + // Double-click (Plotly's usual reset gesture) resets 2D axis ranges but does *not* reset + // the 3D camera, so an explicit control is the only reliable way to get back here. + const plotlyRef = useRef(null); + const graphDivRef = useRef(null); + const handleResetView = () => { + if (!plotlyRef.current || !graphDivRef.current) return; + plotlyRef.current.relayout(graphDivRef.current, { + 'scene.camera.eye': DEFAULT_CAMERA_EYE, + 'scene.xaxis.autorange': true, + 'scene.yaxis.autorange': true, + 'scene.zaxis.autorange': true, + }); + }; + + // Recessive hairline wireframe (one step off the panel surface) with numeric ticks — + // gives the cloud a spatial reference frame instead of floating in a void. A *fresh* + // object per axis, not a shared constant — see EmbeddingPlot2D for why (Plotly's internal + // axis bookkeeping gets confused when multiple axes point at the literal same object). + const makeAxis = () => ({ + showgrid: true, + gridcolor: chrome.grid, + zeroline: false, + showticklabels: true, + linecolor: chrome.grid, + tickfont: { color: chrome.text, family: chrome.fontFamily, size: 10 }, + }); + + return ( +
+ Loading 3D view…
}> + {() => { + const Plotly = require('plotly.js-dist-min'); + plotlyRef.current = Plotly; + const createPlotlyComponent = require('react-plotly.js/factory').default; + const Plot = createPlotlyComponent(Plotly); + return ( + { + graphDivRef.current = graphDiv; + }} + onUpdate={(_figure: unknown, graphDiv: HTMLElement) => { + graphDivRef.current = graphDiv; + }} + layout={{ + autosize: true, + margin: { l: 20, r: 20, t: 15, b: 35 }, + paper_bgcolor: 'transparent', + plot_bgcolor: 'transparent', + scene: { + aspectmode: 'cube', + xaxis: makeAxis(), + yaxis: makeAxis(), + zaxis: makeAxis(), + camera: { eye: DEFAULT_CAMERA_EYE }, + }, + showlegend: false, + hoverlabel: { + bgcolor: chrome.tooltipBg, + bordercolor: chrome.tooltipBorder, + font: { color: chrome.tooltipText }, + }, + // Keeps whatever camera orbit/zoom the user set across re-renders instead of + // snapping back to the default eye position — see EmbeddingPlot2D for why. + uirevision: 'embedding-3d', + }} + config={{ displayModeBar: false, responsive: true }} + style={{ width: '100%', height: '100%' }} + useResizeHandler + /> + ); + }} + + + + ); +} + +export default EmbeddingPlot3D; diff --git a/src/lib/benchmarks.ts b/src/lib/benchmarks.ts index c4b6225..4430a6c 100644 --- a/src/lib/benchmarks.ts +++ b/src/lib/benchmarks.ts @@ -1,6 +1,8 @@ -// Dataset benchmarking metrics, loaded on demand from static/data/benchmarking-result/.json -// (+ an optional .embeddings.json for the UMAP scatter). One task type's metric shape is -// defined below; other task types will get their own shape as their benchmarking pipelines ship. +// Dataset benchmarking metrics, loaded on demand from the shared +// static/data/dataset-benchmarking/results.json (keyed by dataset name), plus optional +// per-dataset UMAP embedding files under static/data/dataset-benchmarking/embeddings/ +// (_umap_2d.json / _umap_3d.json). One task type's metric shape is defined +// below; other task types will get their own shape as their benchmarking pipelines ship. import { useEffect, useState } from "react"; import useBaseUrl from "@docusaurus/useBaseUrl"; @@ -69,8 +71,7 @@ export interface IntraClassDiversityMetrics { export interface ImageClassificationBenchmark { dataset: string; - run_id: string; - phases_completed: number[]; + date: string; metrics: { class_imbalance?: ClassImbalanceMetrics; exact_duplicate?: ExactDuplicateMetrics; @@ -86,22 +87,29 @@ export interface ImageClassificationBenchmark { // metric shapes later. export type BenchmarkData = ImageClassificationBenchmark; +type BenchmarkResults = Record; + +// One row per sample from a UMAP projection. 2D files omit `z`. export interface EmbedPoint { x: number; y: number; - z: number; - class: string; + z?: number; + label: string; + split?: string; + index?: number; } interface BenchmarkState { data: BenchmarkData | null; - embeddings: EmbedPoint[] | null; + embeddings2d: EmbedPoint[] | null; + embeddings3d: EmbedPoint[] | null; loading: boolean; } const EMPTY_STATE: BenchmarkState = { data: null, - embeddings: null, + embeddings2d: null, + embeddings3d: null, loading: false, }; @@ -115,15 +123,26 @@ async function fetchJson(url: string): Promise { } } -// Fetches static/data/benchmarking-result/.json (metrics) and, if present, -// .embeddings.json (UMAP points). Returns null data when no benchmark file exists -// for this dataset (a plain 404, not an error state). +// results.json is one shared file covering every dataset, so it only needs to be +// fetched once per page load rather than once per dataset looked up. +let resultsPromise: Promise | null = null; +function loadResults(url: string): Promise { + if (!resultsPromise) { + resultsPromise = fetchJson(url).then((data) => data ?? {}); + } + return resultsPromise; +} + +// Looks up `datasetName` in the shared results.json and, if found, fetches that dataset's +// 2D and 3D UMAP embedding files. Returns null data when the dataset has no benchmark entry +// (a missing key, not an error state) or when the fetch itself fails. export function useBenchmark(datasetName: string | null): BenchmarkState { - const metricsUrl = useBaseUrl( - `/data/benchmarking-result/${datasetName ?? "_none"}.json`, + const resultsUrl = useBaseUrl("/data/dataset-benchmarking/results.json"); + const embeddings2dUrl = useBaseUrl( + `/data/dataset-benchmarking/embeddings/${datasetName ?? "_none"}_umap_2d.json`, ); - const embeddingsUrl = useBaseUrl( - `/data/benchmarking-result/${datasetName ?? "_none"}.embeddings.json`, + const embeddings3dUrl = useBaseUrl( + `/data/dataset-benchmarking/embeddings/${datasetName ?? "_none"}_umap_3d.json`, ); const [state, setState] = useState(EMPTY_STATE); @@ -133,19 +152,24 @@ export function useBenchmark(datasetName: string | null): BenchmarkState { return; } let cancelled = false; - setState({ data: null, embeddings: null, loading: true }); + setState({ data: null, embeddings2d: null, embeddings3d: null, loading: true }); - fetchJson(metricsUrl).then((data) => { + loadResults(resultsUrl).then((results) => { if (cancelled) return; + const data = results[datasetName] ?? null; if (!data) { - setState({ data: null, embeddings: null, loading: false }); + setState({ data: null, embeddings2d: null, embeddings3d: null, loading: false }); return; } - fetchJson(embeddingsUrl).then((embeddings) => { + Promise.all([ + fetchJson(embeddings2dUrl), + fetchJson(embeddings3dUrl), + ]).then(([embeddings2d, embeddings3d]) => { if (cancelled) return; setState({ data, - embeddings: embeddings ?? null, + embeddings2d: embeddings2d ?? null, + embeddings3d: embeddings3d ?? null, loading: false, }); }); @@ -154,7 +178,7 @@ export function useBenchmark(datasetName: string | null): BenchmarkState { return () => { cancelled = true; }; - }, [datasetName, metricsUrl, embeddingsUrl]); + }, [datasetName, resultsUrl, embeddings2dUrl, embeddings3dUrl]); return state; } diff --git a/src/lib/plotlyChrome.ts b/src/lib/plotlyChrome.ts new file mode 100644 index 0000000..d012c27 --- /dev/null +++ b/src/lib/plotlyChrome.ts @@ -0,0 +1,85 @@ +import { useMemo } from 'react'; +import { useColorMode } from '@docusaurus/theme-common'; + +// Modern browsers serialize getComputedStyle().color back in whatever color space it was +// declared in — the site's --agml-* tokens are oklch(), so a naive resolve returns an +// oklch() string verbatim. Plotly's WebGL/SVG color parsing doesn't understand that syntax, +// so it silently ignores the color and falls back to its own default. Convert explicitly. +// (OKLCH -> OKLab -> linear sRGB -> sRGB, per the CSS Color 4 reference algorithm.) +function oklchToRgb(l: number, c: number, hDeg: number): string { + const hRad = (hDeg * Math.PI) / 180; + const a = c * Math.cos(hRad); + const b = c * Math.sin(hRad); + + const l_ = l + 0.3963377774 * a + 0.2158037573 * b; + const m_ = l - 0.1055613458 * a - 0.0638541728 * b; + const s_ = l - 0.0894841775 * a - 1.291485548 * b; + + const l3 = l_ ** 3; + const m3 = m_ ** 3; + const s3 = s_ ** 3; + + const rLin = 4.0767416621 * l3 - 3.3077115913 * m3 + 0.2309699292 * s3; + const gLin = -1.2684380046 * l3 + 2.6097574011 * m3 - 0.3413193965 * s3; + const bLin = -0.0041960863 * l3 - 0.7034186147 * m3 + 1.707614701 * s3; + + const toSrgb = (channel: number) => { + const clamped = channel <= 0.0031308 ? 12.92 * channel : 1.055 * Math.pow(Math.max(channel, 0), 1 / 2.4) - 0.055; + return Math.max(0, Math.min(255, Math.round(clamped * 255))); + }; + + return `rgb(${toSrgb(rLin)}, ${toSrgb(gLin)}, ${toSrgb(bLin)})`; +} + +function normalizeColor(color: string): string { + const match = /oklch\(\s*([\d.]+)\s+([\d.]+)\s+([\d.]+)/i.exec(color); + if (!match) return color; + const [, l, c, h] = match; + return oklchToRgb(Number(l), Number(c), Number(h)); +} + +function resolveCssColor(varName: string, fallback: string): string { + if (typeof document === 'undefined') return fallback; + const probe = document.createElement('span'); + probe.style.color = `var(${varName})`; + probe.style.position = 'absolute'; + probe.style.visibility = 'hidden'; + document.body.appendChild(probe); + const resolved = getComputedStyle(probe).color; + document.body.removeChild(probe); + return resolved ? normalizeColor(resolved) : fallback; +} + +function resolveCssFontFamily(varName: string, fallback: string): string { + if (typeof document === 'undefined') return fallback; + const probe = document.createElement('span'); + probe.style.fontFamily = `var(${varName})`; + probe.style.position = 'absolute'; + probe.style.visibility = 'hidden'; + document.body.appendChild(probe); + const resolved = getComputedStyle(probe).fontFamily; + document.body.removeChild(probe); + return resolved || fallback; +} + +// Plotly's WebGL/SVG chrome can't consume CSS custom properties directly — resolve the site's +// actual computed colors/fonts so chart chrome (axis lines, grid, tick labels, hover tooltip) +// tracks the design system's --agml-*/--ifm-* tokens (and light/dark mode) instead of guessed +// literals. Recomputed whenever the theme toggles. +export function usePlotlyChrome() { + const { colorMode } = useColorMode(); + return useMemo( + () => ({ + grid: resolveCssColor('--agml-border', '#33383a'), + text: resolveCssColor('--agml-muted', '#8a8f8c'), + tooltipBg: resolveCssColor('--agml-tag-bg', '#1c211d'), + tooltipBorder: resolveCssColor('--agml-border-strong', '#3a4038'), + tooltipText: resolveCssColor('--agml-text', '#e8ece9'), + // Ring color for marker outlines — matches the panel surface so overlapping points + // separate via a gap rather than a contrasting stroke. + markerRing: resolveCssColor('--agml-surface-soft', '#171a17'), + fontFamily: resolveCssFontFamily('--ifm-code-font-family', 'monospace'), + }), + [colorMode], + ); +} diff --git a/static/data/benchmarking-result/watermelon_disease_classification.embeddings.json b/static/data/benchmarking-result/watermelon_disease_classification.embeddings.json deleted file mode 100644 index fce11ff..0000000 --- a/static/data/benchmarking-result/watermelon_disease_classification.embeddings.json +++ /dev/null @@ -1,608 +0,0 @@ -[ - { - "x": -0.5371, - "y": 0.4798, - "z": 0.3412, - "class": "Anthracnose" - }, - { - "x": -0.5837, - "y": 0.4609, - "z": 0.3466, - "class": "Anthracnose" - }, - { - "x": -0.6294, - "y": 0.3709, - "z": 0.1457, - "class": "Anthracnose" - }, - { - "x": -0.6249, - "y": 0.5005, - "z": 0.3123, - "class": "Anthracnose" - }, - { - "x": -0.7338, - "y": 0.4981, - "z": 0.2611, - "class": "Anthracnose" - }, - { - "x": -0.5889, - "y": 0.3672, - "z": 0.3835, - "class": "Anthracnose" - }, - { - "x": -0.5281, - "y": 0.5017, - "z": 0.1415, - "class": "Anthracnose" - }, - { - "x": -0.5228, - "y": 0.5363, - "z": 0.2475, - "class": "Anthracnose" - }, - { - "x": -0.6058, - "y": 0.6525, - "z": 0.2764, - "class": "Anthracnose" - }, - { - "x": -0.5767, - "y": 0.5138, - "z": 0.4689, - "class": "Anthracnose" - }, - { - "x": -0.6355, - "y": 0.4391, - "z": 0.3445, - "class": "Anthracnose" - }, - { - "x": -0.6114, - "y": 0.5564, - "z": 0.2743, - "class": "Anthracnose" - }, - { - "x": -0.6542, - "y": 0.5349, - "z": 0.3018, - "class": "Anthracnose" - }, - { - "x": -0.6212, - "y": 0.4816, - "z": 0.3772, - "class": "Anthracnose" - }, - { - "x": 0.5006, - "y": 0.5414, - "z": -0.5324, - "class": "Downy_Mildew" - }, - { - "x": 0.4931, - "y": 0.5999, - "z": -0.5056, - "class": "Downy_Mildew" - }, - { - "x": 0.4939, - "y": 0.5324, - "z": -0.3516, - "class": "Downy_Mildew" - }, - { - "x": 0.6047, - "y": 0.4079, - "z": -0.4642, - "class": "Downy_Mildew" - }, - { - "x": 0.4786, - "y": 0.3239, - "z": -0.4518, - "class": "Downy_Mildew" - }, - { - "x": 0.662, - "y": 0.529, - "z": -0.2372, - "class": "Downy_Mildew" - }, - { - "x": 0.548, - "y": 0.5799, - "z": -0.3068, - "class": "Downy_Mildew" - }, - { - "x": 0.7042, - "y": 0.4094, - "z": -0.4327, - "class": "Downy_Mildew" - }, - { - "x": 0.7871, - "y": 0.7323, - "z": -0.4228, - "class": "Downy_Mildew" - }, - { - "x": 0.6407, - "y": 0.6372, - "z": -0.4306, - "class": "Downy_Mildew" - }, - { - "x": 0.3842, - "y": 0.7857, - "z": -0.4402, - "class": "Downy_Mildew" - }, - { - "x": 0.3192, - "y": 0.5058, - "z": -0.2236, - "class": "Downy_Mildew" - }, - { - "x": 0.5234, - "y": 0.5215, - "z": -0.4966, - "class": "Downy_Mildew" - }, - { - "x": 0.4212, - "y": 0.6001, - "z": -0.6051, - "class": "Downy_Mildew" - }, - { - "x": 0.4747, - "y": 0.6174, - "z": -0.516, - "class": "Downy_Mildew" - }, - { - "x": 0.5063, - "y": 0.5778, - "z": -0.2429, - "class": "Downy_Mildew" - }, - { - "x": 0.6345, - "y": 0.2949, - "z": -0.5122, - "class": "Downy_Mildew" - }, - { - "x": 0.7067, - "y": 0.385, - "z": -0.3658, - "class": "Downy_Mildew" - }, - { - "x": 0.6835, - "y": 0.5821, - "z": -0.2471, - "class": "Downy_Mildew" - }, - { - "x": 0.5757, - "y": 0.6581, - "z": -0.2741, - "class": "Downy_Mildew" - }, - { - "x": 0.6742, - "y": 0.6974, - "z": -0.3819, - "class": "Downy_Mildew" - }, - { - "x": 0.5795, - "y": 0.6582, - "z": -0.3534, - "class": "Downy_Mildew" - }, - { - "x": 0.6021, - "y": 0.5892, - "z": -0.3691, - "class": "Downy_Mildew" - }, - { - "x": 0.7262, - "y": 0.4487, - "z": -0.2913, - "class": "Downy_Mildew" - }, - { - "x": 0.6112, - "y": 0.3788, - "z": -0.6011, - "class": "Downy_Mildew" - }, - { - "x": 0.7385, - "y": 0.3032, - "z": -0.3277, - "class": "Downy_Mildew" - }, - { - "x": 0.7549, - "y": 0.455, - "z": -0.5113, - "class": "Downy_Mildew" - }, - { - "x": 0.5499, - "y": 0.481, - "z": -0.2025, - "class": "Downy_Mildew" - }, - { - "x": 0.2634, - "y": 0.397, - "z": -0.1977, - "class": "Downy_Mildew" - }, - { - "x": 0.4441, - "y": 0.6408, - "z": -0.4011, - "class": "Downy_Mildew" - }, - { - "x": 0.4375, - "y": 0.6819, - "z": -0.3732, - "class": "Downy_Mildew" - }, - { - "x": 0.5216, - "y": 0.4874, - "z": -0.4227, - "class": "Downy_Mildew" - }, - { - "x": 0.5906, - "y": 0.5093, - "z": -0.3286, - "class": "Downy_Mildew" - }, - { - "x": -0.5651, - "y": -0.4911, - "z": -0.2344, - "class": "Healthy" - }, - { - "x": -0.4924, - "y": -0.5942, - "z": -0.2484, - "class": "Healthy" - }, - { - "x": -0.4563, - "y": -0.4767, - "z": -0.2676, - "class": "Healthy" - }, - { - "x": -0.4696, - "y": -0.6621, - "z": -0.1762, - "class": "Healthy" - }, - { - "x": -0.6105, - "y": -0.5293, - "z": -0.0852, - "class": "Healthy" - }, - { - "x": -0.5488, - "y": -0.5548, - "z": -0.2716, - "class": "Healthy" - }, - { - "x": -0.5152, - "y": -0.5849, - "z": -0.1485, - "class": "Healthy" - }, - { - "x": -0.5141, - "y": -0.5493, - "z": -0.2246, - "class": "Healthy" - }, - { - "x": -0.3814, - "y": -0.5782, - "z": -0.2018, - "class": "Healthy" - }, - { - "x": -0.4648, - "y": -0.4516, - "z": 0.0247, - "class": "Healthy" - }, - { - "x": -0.3303, - "y": -0.5906, - "z": -0.2157, - "class": "Healthy" - }, - { - "x": -0.4556, - "y": -0.4373, - "z": -0.2687, - "class": "Healthy" - }, - { - "x": -0.555, - "y": -0.6288, - "z": -0.124, - "class": "Healthy" - }, - { - "x": -0.4307, - "y": -0.5382, - "z": -0.1613, - "class": "Healthy" - }, 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\ No newline at end of file diff --git a/static/data/dataset-benchmarking/results.json b/static/data/dataset-benchmarking/results.json new file mode 100644 index 0000000..5a5cf5d --- /dev/null +++ b/static/data/dataset-benchmarking/results.json @@ -0,0 +1,94 @@ +{ + "watermelon_disease_classification": { + "dataset": "Project-AgML/watermelon_disease_classification", + "date": "2026-08-03", + "metrics": { + "class_imbalance": { + "counts": { + "Anthracnose": 109, + "Downy_Mildew": 266, + "Healthy": 143, + "Mosaic_Virus": 290 + }, + "imbalance_ratio": 2.6606, + "normalized_entropy": 0.9452, + "most_frequent_class": "Mosaic_Virus", + "least_frequent_class": "Anthracnose", + "total_train_examples": 808 + }, + "exact_duplicate": { + "total_images": 1155, + "exact_duplicate_count": 0, + "exact_duplicate_rate": 0.0, + "duplicate_groups": 0, + "cross_split_duplicates": 0 + }, + "resolution_consistency": { + "total_images": 1155, + "width": { + "mean": 3024, + "std": 0.0, + "min": 3024, + "max": 3024 + }, + "height": { + "mean": 3091.2, + "std": 251.55, + "min": 3024, + "max": 4032 + }, + "aspect_ratio": { + "mean": 0.98, + "std": 0.06, + "min": 0.75, + "max": 1.0 + }, + "area_cv": 0.0814, + "mode_distribution": { + "RGB": 1155 + } + }, + "metadata_coverage": { + "skipped": true, + "reason": "No metadata columns detected in dataset schema." + }, + "near_duplicate": { + "total_images": 1155, + "embed_model": "facebook/dinov2-base", + "threshold": 0.98, + "near_duplicate_count": 46, + "near_duplicate_rate": 0.0398, + "near_duplicate_groups": 22, + "cross_split_near_duplicates": 27, + "faiss_index_type": "IndexFlatIP" + }, + "feature_separability": { + "embed_model": "facebook/dinov2-base", + "n_total": 1155, + "n_silhouette_samples": 1155, + "silhouette_score": 0.2629, + "silhouette_interpretation": "moderate separation", + "davies_bouldin_index": 1.9661, + "davies_bouldin_interpretation": "some overlap between clusters", + "per_class_silhouette": { + "Anthracnose": 0.6231, + "Downy_Mildew": 0.4447, + "Healthy": 0.6217, + "Mosaic_Virus": -0.2152 + } + }, + "intra_class_diversity": { + "embed_model": "facebook/dinov2-base", + "per_class_diversity": { + "Anthracnose": 0.5802, + "Downy_Mildew": 0.3971, + "Healthy": 0.3354, + "Mosaic_Virus": 0.5747 + }, + "mean_diversity": 0.4718, + "min_diversity_class": "Healthy", + "max_diversity_class": "Anthracnose" + } + } + } +} From 72483fc00f77de1b627d9ef1299d6afba71abed7 Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Mon, 3 Aug 2026 19:26:03 -0600 Subject: [PATCH 06/27] 1. Added new benchmarking data. 2. Added support for 30+ classes in the visualization for dataset benchmarking. 3. Added tooltip/help explaning significance for each metric --- .gitignore | 4 +- .../DatasetMetadataModal.module.css | 106 +- src/components/DatasetMetadataModal.tsx | 278 +- src/lib/plotlyChrome.ts | 4 +- ...ACHENY_variety_classification_umap_2d.json | 189188 +++++++++++++ ...ACHENY_variety_classification_umap_3d.json | 216242 +++++++++++++++ ...ety_classification_bangladesh_umap_2d.json | 132750 +++++++++ ...ety_classification_bangladesh_umap_3d.json | 152002 ++++++++++ static/data/dataset-benchmarking/results.json | 365 + 9 files changed, 690861 insertions(+), 78 deletions(-) create mode 100644 static/data/dataset-benchmarking/embeddings/ACHENY_variety_classification_umap_2d.json create mode 100644 static/data/dataset-benchmarking/embeddings/ACHENY_variety_classification_umap_3d.json create mode 100644 static/data/dataset-benchmarking/embeddings/rice_variety_classification_bangladesh_umap_2d.json create mode 100644 static/data/dataset-benchmarking/embeddings/rice_variety_classification_bangladesh_umap_3d.json diff --git a/.gitignore b/.gitignore index a0e3065..1a9a8cb 100644 --- a/.gitignore +++ b/.gitignore @@ -19,4 +19,6 @@ npm-debug.log* yarn-debug.log* yarn-error.log* yarn.lock -.external \ No newline at end of file +.external + +.claude/ \ No newline at end of file diff --git a/src/components/DatasetMetadataModal.module.css b/src/components/DatasetMetadataModal.module.css index fb4489b..47bd044 100644 --- a/src/components/DatasetMetadataModal.module.css +++ b/src/components/DatasetMetadataModal.module.css @@ -165,6 +165,12 @@ .faceHidden { visibility: hidden; pointer-events: none; + /* Absolutely positioned grid items don't contribute to track sizing, so taking the hidden + face out of flow here means the flipper's height always matches whichever face is + actually showing — not the max of both, which left a dead gap under short front-face + content (e.g. an empty leaderboard) whenever the other face was taller. */ + position: absolute; + inset: 0; } .detailGrid { @@ -604,8 +610,14 @@ .metricCardGrid { display: grid; grid-template-columns: repeat(auto-fit, minmax(260px, 1fr)); + /* Cards size to their own content instead of stretching to the tallest card in the row, + so short cards (e.g. a 4-stat metric) don't carry a block of empty space below. */ + align-items: start; gap: 1rem; margin: 0 0 1.75rem; + /* 1-column stat cards backfill the gaps left by 2-column bar cards instead of leaving a + hole and pushing everything after it down to the next row. */ + grid-auto-flow: dense; } .metricCard { @@ -618,6 +630,10 @@ gap: 0.75rem; } +.metricCardSpan2 { + grid-column: span 2; +} + .metricCardHeader { display: flex; align-items: center; @@ -656,6 +672,9 @@ font-family: var(--ifm-code-font-family); font-size: 0.58rem; font-weight: 700; + /* Keep the "i" glyph lowercase even when the icon sits inside an uppercased label. */ + text-transform: none; + letter-spacing: normal; cursor: help; } @@ -722,7 +741,11 @@ .metricBars { display: flex; flex-direction: column; - gap: 0.5rem; + gap: var(--metric-bar-gap); + /* Single source of truth for row height and gap — referenced by .metricBarRow's flex-basis + and by .metricBarsExpanded's cap height below, so the two can't drift out of sync. */ + --metric-bar-row-height: 24px; + --metric-bar-gap: 0.3rem; } .metricBarRow { @@ -730,6 +753,36 @@ grid-template-columns: 112px 1fr 56px; align-items: center; gap: 0.6rem; + flex: 0 0 var(--metric-bar-row-height); + height: var(--metric-bar-row-height); + box-sizing: border-box; + overflow: hidden; +} + +/* Exactly 10 rows visible before scrolling — derived from the same row-height variable the + rows themselves use, so this can't drift from the actual rendered row height. */ +.metricBarsExpanded { + max-height: calc(10 * var(--metric-bar-row-height) + 9 * var(--metric-bar-gap)); + overflow-y: auto; + padding-right: 4px; +} + +.metricExpandLink { + align-self: flex-start; + border: none; + background: none; + padding: 0; + font-family: var(--ifm-code-font-family); + font-size: 0.72rem; + font-weight: 600; + color: var(--ifm-color-primary); + cursor: pointer; +} + +.metricExpandLink:hover, +.metricExpandLink:focus-visible { + text-decoration: underline; + outline: none; } .metricBarLabel { @@ -806,6 +859,9 @@ } .statTileLabel { + display: flex; + align-items: center; + gap: 0.3rem; margin: 0 0 0.3rem; font-family: var(--ifm-code-font-family); font-size: 0.62rem; @@ -823,6 +879,17 @@ overflow-wrap: anywhere; } +/* Interpretation line under a stat's value — smaller and muted so it reads as an explanation + of the number above it, not another value. */ +.statTileHint { + margin: 0.2rem 0 0; + font-size: 0.7rem; + font-weight: 400; + line-height: 1.35; + color: var(--agml-muted); + overflow-wrap: anywhere; +} + .metricFooterStats { display: grid; grid-template-columns: repeat(auto-fit, minmax(105px, 1fr)); @@ -831,6 +898,33 @@ border-top: 1px solid var(--agml-border); } +.metricStatSections { + display: flex; + flex-direction: column; + gap: 0.85rem; +} + +.metricStatSection { + display: flex; + flex-direction: column; + gap: 0.6rem; +} + +.metricStatSection + .metricStatSection { + padding-top: 0.85rem; + border-top: 1px solid var(--agml-border); +} + +.metricStatSectionTitle { + margin: 0; + font-family: var(--ifm-code-font-family); + font-size: 0.66rem; + font-weight: 600; + text-transform: uppercase; + letter-spacing: 0.06em; + color: var(--agml-text); +} + .embedHeader { display: flex; align-items: center; @@ -936,6 +1030,12 @@ flex-shrink: 0; } +.embedLegendOverflow { + font-family: var(--ifm-code-font-family); + font-size: 0.74rem; + color: var(--agml-muted); +} + .embedNote { margin: 0.7rem 0 0; font-size: 0.72rem; @@ -961,6 +1061,10 @@ grid-template-columns: 1fr; } + .metricCardSpan2 { + grid-column: span 1; + } + .metricBarRow { grid-template-columns: 88px 1fr 48px; } diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index d1de8e5..6b01387 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -1,10 +1,12 @@ import { Fragment, useEffect, useMemo, useState } from 'react'; +import { useColorMode } from '@docusaurus/theme-common'; import type { Dataset } from '../lib/datasets'; import { formatDisplayLocation, toTitleCase } from '../lib/datasets'; import { toDisplayLabel } from '../lib/labelOverrides'; import { useBenchmark, type BenchmarkData, type EmbedPoint as RawEmbedPoint } from '../lib/benchmarks'; import { METRIC_CATEGORY_LABELS, useDatasetPerformance } from '../lib/performance'; import type { MetricCategory, PerformanceEntry } from '../lib/performance'; +import { oklchToRgb } from '../lib/plotlyChrome'; import { EmbeddingPlot2D } from './EmbeddingPlot2D'; import { EmbeddingPlot3D } from './EmbeddingPlot3D'; import styles from './DatasetMetadataModal.module.css'; @@ -115,21 +117,27 @@ function formatLoaderInstructions(dataset: Dataset) { }; } -function StatTile({ label, value }: { label: string; value: string }) { +function StatTile({ label, value, hint, info }: { label: string; value: string; hint?: string; info?: string }) { return (
-

{label}

+

+ {label} + {info && } +

{value}

+ {hint &&

{hint}

}
); } type MetricBar = { label: string; value: string; pct: number; positive?: boolean }; -type MetricStat = { label: string; value: string }; +type MetricStat = { label: string; value: string; hint?: string; info?: string }; +type MetricStatSection = { title: string; description: string; badge?: string; stats: MetricStat[] }; type MetricCardVM = | { kind: 'bars'; title: string; description: string; badge?: string; bars: MetricBar[]; footerStats?: MetricStat[] } | { kind: 'signed'; title: string; description: string; badge?: string; bars: MetricBar[]; footerStats?: MetricStat[] } | { kind: 'stats'; title: string; description: string; badge?: string; stats: MetricStat[] } + | { kind: 'stat-sections'; title: string; sections: MetricStatSection[] } | { kind: 'skipped'; title: string; message: string }; function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { @@ -145,50 +153,80 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { description: 'How evenly the classes are represented. Imbalance ratio is the most-frequent class’s count divided by the least-frequent’s (1.0 = perfectly balanced, higher = more skewed). Normalized entropy ranges 0–1, where 1 means every class has an equal share.', badge: `${d.total_train_examples} train examples`, - bars: Object.entries(d.counts).map(([label, value]) => ({ label: toTitleCase(label), value: value.toLocaleString(), pct: (value / max) * 100 })), + bars: Object.entries(d.counts) + .map(([label, value]) => ({ label: toTitleCase(label), value: value.toLocaleString(), pct: (value / max) * 100 })) + .sort((a, b) => b.pct - a.pct), footerStats: [ - { label: 'Imbalance ratio', value: d.imbalance_ratio.toFixed(2) }, - { label: 'Norm. entropy', value: d.normalized_entropy.toFixed(2) }, - { label: 'Most frequent', value: toTitleCase(d.most_frequent_class) }, - { label: 'Least frequent', value: toTitleCase(d.least_frequent_class) }, + { + label: 'Imbalance ratio', + value: d.imbalance_ratio.toFixed(2), + info: 'Most-frequent class count ÷ least-frequent. 1.0 = perfectly balanced; higher = more skewed.', + }, + { + label: 'Norm. entropy', + value: d.normalized_entropy.toFixed(2), + info: 'Normalized entropy, 0–1. 1 = every class has an equal share; lower = more concentrated.', + }, + { label: 'Most frequent', value: toTitleCase(d.most_frequent_class), info: 'Class with the most training examples.' }, + { label: 'Least frequent', value: toTitleCase(d.least_frequent_class), info: 'Class with the fewest training examples.' }, ], }); } - if (m.exact_duplicate) { - const d = m.exact_duplicate; - cards.push({ - kind: 'stats', - title: 'Exact Duplicates', - description: - 'Images that are byte-for-byte identical to another image in the dataset. A high duplicate rate inflates the apparent dataset size and can leak the same example across train/test splits, making evaluation look better than it is.', - badge: `${d.total_images} images`, - stats: [ - { label: 'Duplicate count', value: String(d.exact_duplicate_count) }, - { label: 'Duplicate rate', value: `${(d.exact_duplicate_rate * 100).toFixed(1)}%` }, - { label: 'Groups', value: String(d.duplicate_groups) }, - { label: 'Cross-split', value: String(d.cross_split_duplicates) }, - ], - }); - } + if (m.exact_duplicate || m.near_duplicate) { + const sections: MetricStatSection[] = []; + + if (m.exact_duplicate) { + const d = m.exact_duplicate; + sections.push({ + title: 'Exact', + description: + 'Images that are byte-for-byte identical to another image in the dataset. A high duplicate rate inflates the apparent dataset size and can leak the same example across train/test splits, making evaluation look better than it is.', + badge: `${d.total_images} images`, + stats: [ + { label: 'Duplicate count', value: String(d.exact_duplicate_count), info: 'Images that are byte-for-byte identical to another image in the dataset.' }, + { + label: 'Duplicate rate', + value: `${(d.exact_duplicate_rate * 100).toFixed(1)}%`, + info: 'Share of images that are exact duplicates. A higher rate inflates the apparent dataset size.', + }, + { label: 'Groups', value: String(d.duplicate_groups), info: 'Number of distinct sets of identical images.' }, + { + label: 'Cross-split', + value: String(d.cross_split_duplicates), + info: 'Duplicates that appear across train/test splits — a direct source of evaluation leakage.', + }, + ], + }); + } - if (m.near_duplicate) { - const d = m.near_duplicate; - cards.push({ - kind: 'stats', - title: 'Near Duplicates', - description: - 'Images that are nearly identical (crops, recompressions, small edits) based on embedding similarity above a threshold — not exact byte matches. High near-duplicate rates risk train/test leakage even when the exact-duplicate count is zero.', - badge: d.embed_model, - stats: [ - { label: 'Near-dup count', value: String(d.near_duplicate_count) }, - { label: 'Near-dup rate', value: `${(d.near_duplicate_rate * 100).toFixed(1)}%` }, - { label: 'Groups', value: String(d.near_duplicate_groups) }, - { label: 'Cross-split', value: String(d.cross_split_near_duplicates) }, - { label: 'Threshold', value: String(d.threshold) }, - { label: 'Index type', value: d.faiss_index_type }, - ], - }); + if (m.near_duplicate) { + const d = m.near_duplicate; + sections.push({ + title: 'Near', + description: + 'Images that are nearly identical (crops, recompressions, small edits) based on embedding similarity above a threshold — not exact byte matches. High near-duplicate rates risk train/test leakage even when the exact-duplicate count is zero.', + badge: d.embed_model, + stats: [ + { + label: 'Near-dup count', + value: String(d.near_duplicate_count), + info: 'Images that are nearly identical (crops, recompressions, small edits) by embedding similarity — not exact byte matches.', + }, + { label: 'Near-dup rate', value: `${(d.near_duplicate_rate * 100).toFixed(1)}%`, info: 'Share of images that are near-duplicates.' }, + { label: 'Groups', value: String(d.near_duplicate_groups), info: 'Number of distinct near-duplicate clusters.' }, + { + label: 'Cross-split', + value: String(d.cross_split_near_duplicates), + info: 'Near-duplicates spanning train/test splits — risks leakage even when the exact-duplicate count is zero.', + }, + { label: 'Threshold', value: String(d.threshold), info: 'Cosine-similarity cutoff above which two images count as near-duplicates.' }, + { label: 'Index type', value: d.faiss_index_type, info: 'FAISS index type used for the similarity search.' }, + ], + }); + } + + cards.push({ kind: 'stat-sections', title: 'Duplicate Detection', sections }); } if (m.resolution_consistency) { @@ -200,11 +238,19 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { 'How uniform image dimensions and aspect ratios are across the dataset. Area CV (coefficient of variation) near 0 means sizes barely vary; higher values mean preprocessing has to handle a wide range of source resolutions.', badge: `${d.total_images} images`, stats: [ - { label: 'Width (mean)', value: `${d.width.mean}px` }, - { label: 'Height (mean±std)', value: `${d.height.mean.toFixed(0)}±${d.height.std.toFixed(1)}` }, - { label: 'Aspect ratio', value: `${d.aspect_ratio.mean.toFixed(2)} (${d.aspect_ratio.min.toFixed(2)}–${d.aspect_ratio.max.toFixed(2)})` }, - { label: 'Area CV', value: d.area_cv.toFixed(3) }, - { label: 'Color mode', value: Object.keys(d.mode_distribution).join(', ') }, + { label: 'Width (mean)', value: `${d.width.mean}px`, info: 'Average image width in pixels.' }, + { label: 'Height (mean±std)', value: `${d.height.mean.toFixed(0)}±${d.height.std.toFixed(1)}`, info: 'Average image height ± standard deviation, in pixels.' }, + { + label: 'Aspect ratio', + value: `${d.aspect_ratio.mean.toFixed(2)} (${d.aspect_ratio.min.toFixed(2)}–${d.aspect_ratio.max.toFixed(2)})`, + info: 'Mean aspect ratio, with the min–max range across the dataset in parentheses.', + }, + { + label: 'Area CV', + value: d.area_cv.toFixed(3), + info: 'Coefficient of variation of image area. Near 0 = uniform sizes; higher = a wide range of source resolutions.', + }, + { label: 'Color mode', value: Object.keys(d.mode_distribution).join(', '), info: 'Image color modes present in the dataset (e.g. RGB, L).' }, ], }); } @@ -217,15 +263,27 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { description: 'How distinctly the classes cluster in embedding space. Silhouette score ranges −1 to 1 (higher = better-separated clusters, near 0 = overlapping, negative = points closer to another class than their own — often mislabeled or genuinely ambiguous). Davies-Bouldin index is ≥0 and unbounded, where lower means better separation.', badge: d.embed_model, - bars: Object.entries(d.per_class_silhouette).map(([label, value]) => ({ - label: toTitleCase(label), - value: value.toFixed(2), - pct: Math.abs(value) * 50, - positive: value >= 0, - })), + bars: Object.entries(d.per_class_silhouette) + .map(([label, value]) => ({ + label: toTitleCase(label), + value: value.toFixed(2), + pct: Math.abs(value) * 50, + positive: value >= 0, + })) + .sort((a, b) => b.pct - a.pct), footerStats: [ - { label: 'Silhouette', value: `${d.silhouette_score.toFixed(2)} — ${d.silhouette_interpretation}` }, - { label: 'Davies-Bouldin', value: `${d.davies_bouldin_index.toFixed(2)} — ${d.davies_bouldin_interpretation}` }, + { + label: 'Silhouette', + value: d.silhouette_score.toFixed(2), + hint: d.silhouette_interpretation, + info: 'Ranges −1 to 1. Higher = better-separated clusters; near 0 = overlapping; negative = points closer to another class than their own.', + }, + { + label: 'Davies-Bouldin', + value: d.davies_bouldin_index.toFixed(2), + hint: d.davies_bouldin_interpretation, + info: 'Index ≥ 0 and unbounded. Lower means better separation between classes.', + }, ], }); } @@ -239,11 +297,17 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { description: 'How visually varied the images within each class are, based on average embedding distance between same-class examples. Higher values mean more diverse, less redundant examples per class; a very low value can mean that class is dominated by near-duplicates.', badge: d.embed_model, - bars: Object.entries(d.per_class_diversity).map(([label, value]) => ({ label: toTitleCase(label), value: value.toFixed(2), pct: (value / max) * 100 })), + bars: Object.entries(d.per_class_diversity) + .map(([label, value]) => ({ label: toTitleCase(label), value: value.toFixed(2), pct: (value / max) * 100 })) + .sort((a, b) => b.pct - a.pct), footerStats: [ - { label: 'Mean diversity', value: d.mean_diversity.toFixed(2) }, - { label: 'Min class', value: toTitleCase(d.min_diversity_class) }, - { label: 'Max class', value: toTitleCase(d.max_diversity_class) }, + { + label: 'Mean diversity', + value: d.mean_diversity.toFixed(2), + info: 'Average embedding distance between same-class images, averaged over classes. Higher = more varied, less redundant.', + }, + { label: 'Min class', value: toTitleCase(d.min_diversity_class), info: 'Class with the least visual variety — possibly dominated by near-duplicates.' }, + { label: 'Max class', value: toTitleCase(d.max_diversity_class), info: 'Class with the most visual variety.' }, ], }); } @@ -266,23 +330,60 @@ function InfoTooltip({ text }: { text: string }) { ); } +// Above this many bars, a card shows only the top BAR_CAP (already sorted descending in +// buildMetricCards) with a "Show all N →" link instead of every class at once. +const BAR_CAP = 8; + +// The three bar-chart-style cards read better wider (long class lists, signed diverging bars), +// so they span 2 grid columns while the stat-only cards stay at 1 — paired with +// grid-auto-flow: dense on .metricCardGrid so 1-column cards backfill the gaps. +function isBarCard(card: MetricCardVM): boolean { + return card.kind === 'bars' || card.kind === 'signed'; +} + function MetricCard({ card }: { card: MetricCardVM }) { - const badge = card.kind !== 'skipped' ? card.badge : undefined; + const [expanded, setExpanded] = useState(false); + const badge = card.kind !== 'skipped' && card.kind !== 'stat-sections' ? card.badge : undefined; + const allBars = card.kind === 'bars' || card.kind === 'signed' ? card.bars : undefined; + const hasMoreBars = Boolean(allBars && allBars.length > BAR_CAP); + const visibleBars = allBars ? (expanded || !hasMoreBars ? allBars : allBars.slice(0, BAR_CAP)) : undefined; + return ( -
+

{card.title}

- {card.kind !== 'skipped' && } + {card.kind !== 'skipped' && card.kind !== 'stat-sections' && }
{badge && {badge}}
{card.kind === 'skipped' &&

Skipped — {card.message}

} - {(card.kind === 'bars' || card.kind === 'signed') && ( -
- {card.bars.map((bar) => ( + {card.kind === 'stat-sections' && ( +
+ {card.sections.map((section) => ( +
+
+
+
{section.title}
+ +
+ {section.badge && {section.badge}} +
+
+ {section.stats.map((stat) => ( + + ))} +
+
+ ))} +
+ )} + + {visibleBars && ( +
+ {visibleBars.map((bar) => (
{bar.label} {card.kind === 'signed' ? ( @@ -308,10 +409,16 @@ function MetricCard({ card }: { card: MetricCardVM }) {
)} + {hasMoreBars && allBars && ( + + )} + {card.kind === 'stats' && (
{card.stats.map((stat) => ( - + ))}
)} @@ -319,7 +426,7 @@ function MetricCard({ card }: { card: MetricCardVM }) { {(card.kind === 'bars' || card.kind === 'signed') && card.footerStats && card.footerStats.length > 0 && (
{card.footerStats.map((stat) => ( - + ))}
)} @@ -327,7 +434,16 @@ function MetricCard({ card }: { card: MetricCardVM }) { ); } -const PALETTE = ['#4C9BE8', '#F4A261', '#2EC4B6', '#E63946', '#A8DADC', '#8ECAE6', '#FFB4A2', '#B5838D']; +// A fixed palette repeats (or blurs together) past a handful of classes, so class colors are +// generated procedurally instead: step the hue by 47° per class (coprime-ish with 360, so +// hues don't cluster or repeat for a long time) at a lightness/chroma tuned per theme. +function classColor(index: number, isDark: boolean): string { + const hue = (index * 47) % 360; + const lightness = isDark ? 0.72 : 0.5; + const chroma = isDark ? 0.13 : 0.12; + return oklchToRgb(lightness, chroma, hue); +} + const EMBED_CLUSTER_CENTERS: [number, number, number][] = [ [-0.6, 0.5, 0.3], [0.55, 0.55, -0.4], @@ -397,11 +513,15 @@ function buildEmbeddingPoints(benchmark: BenchmarkData): EmbedPoint[] { return points; } -function buildClassColorMap(points: EmbedPoint[]): Record { +function buildClassColorMap(points: EmbedPoint[], isDark: boolean): Record { const labels = Array.from(new Set(points.map((p) => p.cls))).sort(); - return Object.fromEntries(labels.map((label, i) => [label, PALETTE[i % PALETTE.length]])); + return Object.fromEntries(labels.map((label, i) => [label, classColor(i, isDark)])); } +// Beyond this many classes, the legend switches from listing every one to a "+N more" note — +// past ~10 swatches a legend stops being scannable anyway. +const LEGEND_CAP = 10; + function EmbeddingScatter({ benchmark, embeddings2d, @@ -412,6 +532,7 @@ function EmbeddingScatter({ embeddings3d: RawEmbedPoint[] | null; }) { const [view, setView] = useState<'2d' | '3d'>('2d'); + const { colorMode } = useColorMode(); // 2D and 3D UMAP projections are independently fit, so they're separate point sets — // not the same points with one axis dropped. @@ -422,7 +543,7 @@ function EmbeddingScatter({ return buildEmbeddingPoints(benchmark); }, [activeRaw, benchmark]); - const colorMap = useMemo(() => buildClassColorMap(points), [points]); + const colorMap = useMemo(() => buildClassColorMap(points, colorMode === 'dark'), [points, colorMode]); const embedModel = benchmark.metrics.feature_separability?.embed_model ?? 'dinov2-base'; @@ -458,12 +579,19 @@ function EmbeddingScatter({ )}
- {Object.entries(colorMap).map(([key, color]) => ( - - - {toTitleCase(key)} + {Object.entries(colorMap) + .slice(0, LEGEND_CAP) + .map(([key, color]) => ( + + + {toTitleCase(key)} + + ))} + {Object.keys(colorMap).length > LEGEND_CAP && ( + + +{Object.keys(colorMap).length - LEGEND_CAP} more (hover points for class) - ))} + )}

{hasRealEmbeddings diff --git a/src/lib/plotlyChrome.ts b/src/lib/plotlyChrome.ts index d012c27..0099e60 100644 --- a/src/lib/plotlyChrome.ts +++ b/src/lib/plotlyChrome.ts @@ -6,7 +6,9 @@ import { useColorMode } from '@docusaurus/theme-common'; // oklch() string verbatim. Plotly's WebGL/SVG color parsing doesn't understand that syntax, // so it silently ignores the color and falls back to its own default. Convert explicitly. // (OKLCH -> OKLab -> linear sRGB -> sRGB, per the CSS Color 4 reference algorithm.) -function oklchToRgb(l: number, c: number, hDeg: number): string { +// Exported for reuse by anything that needs to hand Plotly a procedurally generated OKLCH +// color (e.g. per-class hues) rather than one resolved from a CSS variable. +export function oklchToRgb(l: number, c: number, hDeg: number): string { const hRad = (hDeg * Math.PI) / 180; const a = c * Math.cos(hRad); const b = c * Math.sin(hRad); diff --git a/static/data/dataset-benchmarking/embeddings/ACHENY_variety_classification_umap_2d.json b/static/data/dataset-benchmarking/embeddings/ACHENY_variety_classification_umap_2d.json new file mode 100644 index 0000000..0cab599 --- /dev/null +++ b/static/data/dataset-benchmarking/embeddings/ACHENY_variety_classification_umap_2d.json @@ -0,0 +1,189188 @@ +[ + { + "x": 8.0738, + "y": 17.4759, + "label": "AnaHau", + "split": "train", + "index": 0 + }, + { + "x": 8.5013, + "y": 17.354, + "label": "AnaHau", + "split": "test", + "index": 1 + }, + { + "x": 7.9579, + "y": 17.6188, + "label": "AnaHau", + "split": "train", + "index": 2 + }, + { + "x": 8.4359, + "y": 17.2972, + "label": "AnaHau", + "split": "train", + "index": 3 + }, + { + "x": 8.4113, + "y": 17.2564, + "label": "AnaHau", + "split": "test", + "index": 4 + }, + { + "x": 8.2912, + "y": 17.4221, + "label": "AnaHau", + "split": "val", + "index": 5 + }, + { + "x": 8.4193, + "y": 17.5864, + "label": "AnaHau", + "split": "train", + "index": 6 + }, + { + "x": 8.4819, + "y": 17.3651, + "label": "AnaHau", + "split": "train", + "index": 7 + }, + { + "x": 8.5486, + "y": 17.2347, + "label": "AnaHau", + "split": "train", + "index": 8 + }, + { + "x": 8.5812, + "y": 17.1967, + "label": "AnaHau", + "split": "train", + "index": 9 + }, + { + "x": 8.108, + "y": 17.1905, + "label": "AnaHau", + "split": "test", + "index": 10 + }, + { + "x": 8.3812, + "y": 17.1582, + "label": "AnaHau", + "split": "train", + "index": 11 + }, + { + "x": 8.3927, + "y": 17.2214, + "label": "AnaHau", + "split": "train", + "index": 12 + }, + { + "x": 8.3814, + "y": 17.4096, + "label": "AnaHau", + "split": "test", + "index": 13 + }, + { + "x": 8.4042, + "y": 17.382, + "label": "AnaHau", + "split": "train", + "index": 14 + }, + { + "x": 8.3444, + "y": 17.4714, + "label": "AnaHau", + "split": "test", + "index": 15 + }, + { + "x": 8.4802, + "y": 17.4258, + "label": "AnaHau", + "split": "train", + "index": 16 + }, + { + "x": 8.0282, + "y": 17.4539, + "label": "AnaHau", + "split": "train", + "index": 17 + }, + { + "x": 8.1669, + "y": 17.2751, + "label": "AnaHau", + "split": "test", + "index": 18 + }, + { + "x": 8.345, + "y": 17.5423, + "label": "AnaHau", + "split": "train", + "index": 19 + }, + { + "x": 8.3636, + "y": 17.5671, + "label": "AnaHau", + "split": "train", + "index": 20 + }, + { + "x": 8.2577, + "y": 17.5597, + "label": "AnaHau", + "split": "train", + "index": 21 + }, + { + "x": 8.4909, + "y": 17.4337, + "label": 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2.9621, + "label": "BR23", + "split": "train", + "index": 18986 + }, + { + "x": 1.0607, + "y": -2.0448, + "z": 3.0351, + "label": "BR23", + "split": "train", + "index": 18987 + }, + { + "x": -0.3385, + "y": 0.2981, + "z": 4.3573, + "label": "BR23", + "split": "val", + "index": 18988 + }, + { + "x": -0.237, + "y": -1.3046, + "z": 1.6155, + "label": "BR23", + "split": "train", + "index": 18989 + }, + { + "x": 2.0489, + "y": -1.2045, + "z": 3.8665, + "label": "BR23", + "split": "train", + "index": 18990 + }, + { + "x": -0.6983, + "y": -0.9859, + "z": 3.0865, + "label": "BR23", + "split": "val", + "index": 18991 + }, + { + "x": 1.1835, + "y": -1.7886, + "z": 2.1208, + "label": "BR23", + "split": "test", + "index": 18992 + }, + { + "x": 1.7988, + "y": -0.3545, + "z": 3.9502, + "label": "BR23", + "split": "train", + "index": 18993 + }, + { + "x": 2.1911, + "y": -1.7717, + "z": 2.971, + "label": "BR23", + "split": "test", + "index": 18994 + }, + { + "x": 2.3687, + "y": -0.4425, + "z": 3.5614, + "label": "BR23", + "split": "train", + "index": 18995 + }, + { + "x": 1.83, + "y": -2.0398, + "z": 3.0719, + "label": "BR23", + "split": "val", + "index": 18996 + }, + { + "x": -0.3671, + "y": -0.9712, + "z": 2.6482, + "label": "BR23", + "split": "train", + "index": 18997 + }, + { + "x": 2.0089, + "y": -0.3898, + "z": 3.6434, + "label": "BR23", + "split": "test", + "index": 18998 + }, + { + "x": 1.3637, + "y": -0.537, + "z": 3.9184, + "label": "BR23", + "split": "train", + "index": 18999 + } +] diff --git a/static/data/dataset-benchmarking/results.json b/static/data/dataset-benchmarking/results.json index 5a5cf5d..a18f8f5 100644 --- a/static/data/dataset-benchmarking/results.json +++ b/static/data/dataset-benchmarking/results.json @@ -90,5 +90,370 @@ "max_diversity_class": "Anthracnose" } } + }, + "rice_variety_classification_bangladesh": { + "dataset": "Project-AgML/rice_variety_classification_bangladesh", + "date": "2026-08-03", + "metrics": { + "class_imbalance": { + "counts": { + "BD30": 350, + "BD33": 350, + "BD39": 350, + "BD49": 350, + "BD51": 350, + "BD52": 350, + "BD56": 350, + "BD57": 350, + "BD70": 350, + "BD72": 350, + "BD75": 350, + "BD76": 350, + "BD79": 350, + "BD85": 350, + "BD87": 350, + "BD91": 350, + "BD93": 350, + "BD95": 350, + "BR22": 350, + "BR23": 350, + "BRRI102": 350, + "BRRI67": 350, + "BRRI74": 350, + "Binadhan10": 350, + "Binadhan11": 350, + "Binadhan12": 350, + "Binadhan14": 350, + "Binadhan16": 350, + "Binadhan17": 350, + "Binadhan19": 350, + "Binadhan20": 350, + "Binadhan21": 350, + "Binadhan23": 350, + "Binadhan24": 350, + "Binadhan25": 349, + "Binadhan26": 350, + "Binadhan7": 350, + "Binadhan8": 350 + }, + "imbalance_ratio": 1.0029, + "normalized_entropy": 1.0, + "most_frequent_class": "BD30", + "least_frequent_class": "Binadhan25", + "total_train_examples": 13299 + }, + "exact_duplicate": { + "total_images": 19000, + "exact_duplicate_count": 0, + "exact_duplicate_rate": 0.0, + "duplicate_groups": 0, + "cross_split_duplicates": 0 + }, + "resolution_consistency": { + "total_images": 19000, + "width": { + "mean": 640, + "std": 0.0, + "min": 640, + "max": 640 + }, + "height": { + "mean": 480, + "std": 0.0, + "min": 480, + "max": 480 + }, + "aspect_ratio": { + "mean": 1.33, + "std": 0.0, + "min": 1.3333333333333333, + "max": 1.3333333333333333 + }, + "area_cv": 0.0, + "mode_distribution": { + "RGBA": 7500, + "RGB": 11500 + } + }, + "metadata_coverage": { + "skipped": true, + "reason": "No metadata columns detected in dataset schema." + }, + "near_duplicate": { + "total_images": 19000, + "embed_model": "facebook/dinov2-base", + "threshold": 0.98, + "near_duplicate_count": 36, + "near_duplicate_rate": 0.0019, + "near_duplicate_groups": 18, + "cross_split_near_duplicates": 20, + "faiss_index_type": "IndexFlatIP" + }, + "feature_separability": { + "embed_model": "facebook/dinov2-base", + "n_total": 19000, + "n_silhouette_samples": 9994, + "silhouette_score": -0.0172, + "silhouette_interpretation": "overlapping or no separation", + "davies_bouldin_index": 6.277, + "davies_bouldin_interpretation": "high overlap \u2014 classes not well-separated", + "per_class_silhouette": { + "BD30": -0.0048, + "BD33": -0.0348, + "BD39": 0.0449, + "BD49": -0.0861, + "BD51": -0.0316, + "BD52": -0.0291, + "BD56": -0.0434, + "BD57": -0.0479, + "BD70": -0.0623, + "BD72": 0.0167, + "BD75": -0.0805, + "BD76": 0.0543, + "BD79": -0.0197, + "BD85": -0.1005, + "BD87": 0.0829, + "BD91": -0.0505, + "BD93": -0.0357, + "BD95": -0.1184, + "BR22": 0.0376, + "BR23": 0.0062, + "BRRI102": -0.079, + "BRRI67": -0.0374, + "BRRI74": 0.0222, + "Binadhan10": 0.0131, + "Binadhan11": 0.0264, + "Binadhan12": -0.0271, + "Binadhan14": -0.0819, + "Binadhan16": -0.0121, + "Binadhan17": -0.0136, + "Binadhan19": -0.0176, + "Binadhan20": 0.2056, + "Binadhan21": -0.036, + "Binadhan23": -0.0188, + "Binadhan24": -0.0521, + "Binadhan25": -0.0142, + "Binadhan26": -0.0373, + "Binadhan7": 0.0181, + "Binadhan8": -0.009 + } + }, + "intra_class_diversity": { + "embed_model": "facebook/dinov2-base", + "per_class_diversity": { + "BD30": 0.4799, + "BD33": 0.5027, + "BD39": 0.492, + "BD49": 0.516, + "BD51": 0.4809, + "BD52": 0.4774, + "BD56": 0.4863, + "BD57": 0.4993, + "BD70": 0.5314, + "BD72": 0.4583, + "BD75": 0.5077, + "BD76": 0.4541, + "BD79": 0.4732, + "BD85": 0.5212, + "BD87": 0.4587, + "BD91": 0.4851, + "BD93": 0.4751, + "BD95": 0.5115, + "BR22": 0.4446, + "BR23": 0.4636, + "BRRI102": 0.5574, + "BRRI67": 0.5032, + "BRRI74": 0.4858, + "Binadhan10": 0.4951, + "Binadhan11": 0.4708, + "Binadhan12": 0.4991, + "Binadhan14": 0.5032, + "Binadhan16": 0.488, + "Binadhan17": 0.4875, + "Binadhan19": 0.4997, + "Binadhan20": 0.523, + "Binadhan21": 0.4837, + "Binadhan23": 0.4981, + "Binadhan24": 0.507, + "Binadhan25": 0.519, + "Binadhan26": 0.4948, + "Binadhan7": 0.4746, + "Binadhan8": 0.511 + }, + "mean_diversity": 0.4926, + "min_diversity_class": "BR22", + "max_diversity_class": "BRRI102" + } + } + }, + "ACHENY_variety_classification": { + "dataset": "Project-AgML/ACHENY_variety_classification", + "date": "2026-08-03", + "metrics": { + "class_imbalance": { + "counts": { + "AnaHau": 538, + "AnaSet": 897, + "AtrCan": 505, + "AtrLen": 936, + "AtrLeu": 440, + "CerAre": 565, + "CheAlb": 456, + "CorMon": 769, + "GirOpp": 217, + "HalAmm": 650, + "HalBel": 440, + "HalPer": 812, + "HalStr": 594, + "HalSub": 677, + "HamSal": 834, + "KocSco": 637, + "KocSte": 596, + "SalAba": 513, + "SalDen": 1023, + "SalInc": 812, + "SalKal": 210, + "SalKer": 633, + "SalPra": 650, + "SalTom": 678, + "SalTur": 685, + "SalYaz": 734, + "SeiCin": 385, + "SeiRos": 700, + "SuaAcu": 820, + "SuaAeg": 514 + }, + "imbalance_ratio": 4.8714, + "normalized_entropy": 0.9857, + "most_frequent_class": "SalDen", + "least_frequent_class": "SalKal", + "total_train_examples": 18920 + }, + "exact_duplicate": { + "total_images": 27030, + "exact_duplicate_count": 0, + "exact_duplicate_rate": 0.0, + "duplicate_groups": 0, + "cross_split_duplicates": 0 + }, + "resolution_consistency": { + "total_images": 27030, + "width": { + "mean": 224, + "std": 0.0, + "min": 224, + "max": 224 + }, + "height": { + "mean": 224, + "std": 0.0, + "min": 224, + "max": 224 + }, + "aspect_ratio": { + "mean": 1.0, + "std": 0.0, + "min": 1.0, + "max": 1.0 + }, + "area_cv": 0.0, + "mode_distribution": { + "RGB": 27030 + } + }, + "metadata_coverage": { + "skipped": true, + "reason": "No metadata columns detected in dataset schema." + }, + "near_duplicate": { + "total_images": 27030, + "embed_model": "facebook/dinov2-base", + "threshold": 0.98, + "near_duplicate_count": 8, + "near_duplicate_rate": 0.0003, + "near_duplicate_groups": 4, + "cross_split_near_duplicates": 6, + "faiss_index_type": "IndexFlatIP" + }, + "feature_separability": { + "embed_model": "facebook/dinov2-base", + "n_total": 27030, + "n_silhouette_samples": 10000, + "silhouette_score": 0.0851, + "silhouette_interpretation": "weak separation", + "davies_bouldin_index": 2.9768, + "davies_bouldin_interpretation": "high overlap \u2014 classes not well-separated", + "per_class_silhouette": { + "AnaHau": 0.1234, + "AnaSet": 0.2493, + "AtrCan": 0.0097, + "AtrLen": 0.0487, + "AtrLeu": 0.0222, + "CerAre": 0.3038, + "CheAlb": 0.1869, + "CorMon": 0.1051, + "GirOpp": 0.221, + "HalAmm": 0.0616, + "HalBel": 0.2117, + "HalPer": 0.0156, + "HalStr": 0.2143, + "HalSub": -0.0415, + "HamSal": 0.1021, + "KocSco": -0.0639, + "KocSte": 0.203, + "SalAba": -0.0611, + "SalDen": -0.0028, + "SalInc": -0.0389, + "SalKal": 0.0784, + "SalKer": 0.0476, + "SalPra": 0.3901, + "SalTom": 0.2113, + "SalTur": -0.0844, + "SalYaz": 0.0653, + "SeiCin": 0.0781, + "SeiRos": 0.0033, + "SuaAcu": 0.0433, + "SuaAeg": 0.0561 + } + }, + "intra_class_diversity": { + "embed_model": "facebook/dinov2-base", + "per_class_diversity": { + "AnaHau": 0.5733, + "AnaSet": 0.5439, + "AtrCan": 0.6505, + "AtrLen": 0.631, + "AtrLeu": 0.5972, + "CerAre": 0.5711, + "CheAlb": 0.7238, + "CorMon": 0.5252, + "GirOpp": 0.5125, + "HalAmm": 0.6213, + "HalBel": 0.5918, + "HalPer": 0.5619, + "HalStr": 0.6569, + "HalSub": 0.5981, + "HamSal": 0.5068, + "KocSco": 0.6997, + "KocSte": 0.5007, + "SalAba": 0.624, + "SalDen": 0.5772, + "SalInc": 0.6843, + "SalKal": 0.6593, + "SalKer": 0.5971, + "SalPra": 0.5797, + "SalTom": 0.5858, + "SalTur": 0.7058, + "SalYaz": 0.512, + "SeiCin": 0.6223, + "SeiRos": 0.5881, + "SuaAcu": 0.6384, + "SuaAeg": 0.5694 + }, + "mean_diversity": 0.6003, + "min_diversity_class": "KocSte", + "max_diversity_class": "CheAlb" + } + } } } From 7b8cec0a7073c4301039f4329fec11f86def9b64 Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Wed, 12 Aug 2026 22:21:29 -0700 Subject: [PATCH 07/27] Add scoring formulas and phase 3 metric types for dataset benchmarking Implements the axis scoring math from SCORING_FORMULAS.md in its own module, and extends the benchmark data types with reproducibility info, phases_completed, and the phase 3 metrics (dataset cartography, class confusability, label noise). Updates results.json to the new schema and refreshes the watermelon dataset's benchmark run with real phase 2 data. --- src/lib/benchmarks.ts | 58 ++++++++- src/lib/scoring.ts | 87 +++++++++++++ static/data/dataset-benchmarking/results.json | 114 +++++++++++++++++- 3 files changed, 255 insertions(+), 4 deletions(-) create mode 100644 src/lib/scoring.ts diff --git a/src/lib/benchmarks.ts b/src/lib/benchmarks.ts index 4430a6c..f1987ec 100644 --- a/src/lib/benchmarks.ts +++ b/src/lib/benchmarks.ts @@ -69,17 +69,73 @@ export interface IntraClassDiversityMetrics { max_diversity_class: string; } +export interface DatasetCartographyMetrics { + n_easy: number; + n_ambiguous: number; + n_hard: number; + pct_easy: number; + pct_ambiguous: number; + pct_hard: number; + mean_confidence: number; + mean_variability: number; + easy_threshold: number; + hard_threshold: number; + variability_threshold: number; + n_epochs: number; +} + +export interface ConfusedPair { + true_class: string; + predicted_as: string; + confusion_rate: number; +} + +export interface ClassConfusabilityMetrics { + accuracy: number; + per_class_accuracy: Record; + confusion_matrix: number[][]; + top_confused_pairs: ConfusedPair[]; + n_top_pairs: number; + n_test_samples: number; +} + +export interface LabelNoiseMetrics { + estimated_noise_rate: number; + n_noisy_samples: number; + n_total_samples: number; + per_class_noise_counts: Record; + cv_folds: number; +} + +export interface ReproducibilityInfo { + split_seed?: number; + train_ratio?: number; + val_ratio?: number; + test_ratio?: number; + embed_model?: string; + near_dup_threshold?: number; + backbone?: string; + cartography_epochs?: number; + cartography_lr?: number; + cv_folds?: number; +} + export interface ImageClassificationBenchmark { dataset: string; date: string; + reproducibility?: ReproducibilityInfo; + phases_completed?: number[]; metrics: { class_imbalance?: ClassImbalanceMetrics; exact_duplicate?: ExactDuplicateMetrics; resolution_consistency?: ResolutionConsistencyMetrics; - metadata_coverage?: { skipped: boolean; reason?: string }; + metadata_coverage?: { skipped: boolean; reason?: string; metadata_columns?: string[]; normalized_entropy?: Record> }; near_duplicate?: NearDuplicateMetrics; feature_separability?: FeatureSeparabilityMetrics; intra_class_diversity?: IntraClassDiversityMetrics; + dataset_cartography?: DatasetCartographyMetrics; + class_confusability?: ClassConfusabilityMetrics; + label_noise?: LabelNoiseMetrics; }; } diff --git a/src/lib/scoring.ts b/src/lib/scoring.ts new file mode 100644 index 0000000..7b0a1a0 --- /dev/null +++ b/src/lib/scoring.ts @@ -0,0 +1,87 @@ +// Implements the scoring formulas from SCORING_FORMULAS.md against a benchmark report. Kept +// separate from benchmarks.ts (data fetching) and DatasetMetadataModal.tsx (rendering) so the +// math has one source of truth that matches the published doc line for line. +import type { ImageClassificationBenchmark } from './benchmarks'; + +export interface AxisScores { + overall: number | null; + structural: number | null; + difficulty: number | null; + diversity: number | null; + annotation: number | null; +} + +function clamp(x: number, min: number, max: number): number { + return Math.min(Math.max(x, min), max); +} + +// A metric is "available" if its key exists in `metrics` and isn't a skipped run (e.g. +// metadata_coverage with no metadata columns detected). +function has(metrics: ImageClassificationBenchmark['metrics'], key: keyof ImageClassificationBenchmark['metrics']): boolean { + const value = metrics[key]; + return value != null && !(value as { skipped?: boolean }).skipped; +} + +export function computeScores(benchmark: ImageClassificationBenchmark): AxisScores { + const m = benchmark.metrics; + const phases = benchmark.phases_completed ?? []; + const p1 = phases.includes(1); + const p2 = phases.includes(2); + const p3 = phases.includes(3); + + const axes: Partial> = {}; + + // Axis 1 — Structural Quality + if (p1 && has(m, 'class_imbalance') && has(m, 'exact_duplicate') && has(m, 'near_duplicate') && has(m, 'resolution_consistency')) { + const balance = clamp(m.class_imbalance!.normalized_entropy * 10, 0, 10); + const combined = m.exact_duplicate!.exact_duplicate_rate + m.near_duplicate!.near_duplicate_rate; + const redundancy = clamp(10 * (1 - combined / 0.2), 0, 10); + const resolution = clamp(10 * (1 - m.resolution_consistency!.area_cv), 0, 10); + const crossSplit = m.exact_duplicate!.cross_split_duplicates + m.near_duplicate!.cross_split_near_duplicates; + const penalty = clamp((crossSplit * 100) / m.exact_duplicate!.total_images, 0, 2); + axes.structural = clamp(0.4 * balance + 0.35 * redundancy + 0.25 * resolution - penalty, 0, 10); + } + + // Axis 2 — Content Difficulty + if (p2 && has(m, 'feature_separability')) { + const sep = m.feature_separability!; + const sil = clamp(sep.silhouette_score * 10, 0, 10); + const db = clamp(10 - sep.davies_bouldin_index * 3, 0, 10); + const separability = (sil + db) / 2; + if (p3 && has(m, 'dataset_cartography') && has(m, 'class_confusability')) { + const cart = clamp(10 * (1 - m.dataset_cartography!.pct_hard / 50), 0, 10); + const conf = clamp(m.class_confusability!.accuracy * 10, 0, 10); + axes.difficulty = 0.4 * separability + 0.3 * cart + 0.3 * conf; + } else { + axes.difficulty = separability; + } + } + + // Axis 3 — Diversity & Coverage + if (p2 && has(m, 'intra_class_diversity')) { + axes.diversity = clamp((m.intra_class_diversity!.mean_diversity / 0.4) * 10, 0, 10); + } + + // Axis 4 — Annotation Reliability + if (p3 && has(m, 'label_noise')) { + axes.annotation = clamp(10 * Math.pow(1 - m.label_noise!.estimated_noise_rate / 0.1, 1.5), 0, 10); + } + + const weights: Record = { structural: 0.3, difficulty: 0.25, diversity: 0.25, annotation: 0.2 }; + let weightedSum = 0; + let totalWeight = 0; + for (const [axis, score] of Object.entries(axes)) { + weightedSum += score * weights[axis]; + totalWeight += weights[axis]; + } + const overall = totalWeight > 0 ? weightedSum / totalWeight : null; + + const round1 = (value: number | undefined) => (value != null ? Math.round(value * 10) / 10 : null); + return { + overall: round1(overall ?? undefined), + structural: round1(axes.structural), + difficulty: round1(axes.difficulty), + diversity: round1(axes.diversity), + annotation: round1(axes.annotation), + }; +} diff --git a/static/data/dataset-benchmarking/results.json b/static/data/dataset-benchmarking/results.json index a18f8f5..54fc55e 100644 --- a/static/data/dataset-benchmarking/results.json +++ b/static/data/dataset-benchmarking/results.json @@ -1,7 +1,23 @@ { "watermelon_disease_classification": { "dataset": "Project-AgML/watermelon_disease_classification", - "date": "2026-08-03", + "date": "2026-08-12", + "reproducibility": { + "split_seed": 42, + "train_ratio": 0.7, + "val_ratio": 0.15, + "test_ratio": 0.15, + "embed_model": "facebook/dinov2-base", + "near_dup_threshold": 0.98, + "backbone": "resnet18", + "cartography_epochs": 30, + "cartography_lr": 0.001, + "cv_folds": 5 + }, + "phases_completed": [ + 1, + 2 + ], "metrics": { "class_imbalance": { "counts": { @@ -88,12 +104,92 @@ "mean_diversity": 0.4718, "min_diversity_class": "Healthy", "max_diversity_class": "Anthracnose" + }, + "dataset_cartography": { + "n_easy": 733, + "n_ambiguous": 75, + "n_hard": 0, + "pct_easy": 90.72, + "pct_ambiguous": 9.28, + "pct_hard": 0.0, + "mean_confidence": 0.9917, + "mean_variability": 0.031, + "easy_threshold": 0.7, + "hard_threshold": 0.3, + "variability_threshold": 0.1, + "n_epochs": 30 + }, + "class_confusability": { + "accuracy": 0.7471, + "per_class_accuracy": { + "Anthracnose": 0.9565, + "Downy_Mildew": 1.0, + "Healthy": 1.0, + "Mosaic_Virus": 0.3175 + }, + "confusion_matrix": [ + [ + 0.9565, + 0.0, + 0.0, + 0.0435 + ], + [ + 0.0, + 1.0, + 0.0, + 0.0 + ], + [ + 0.0, + 0.0, + 1.0, + 0.0 + ], + [ + 0.0, + 0.4921, + 0.1905, + 0.3175 + ] + ], + "top_confused_pairs": [ + { + "true_class": "Mosaic_Virus", + "predicted_as": "Downy_Mildew", + "confusion_rate": 0.4921 + }, + { + "true_class": "Mosaic_Virus", + "predicted_as": "Healthy", + "confusion_rate": 0.1905 + }, + { + "true_class": "Anthracnose", + "predicted_as": "Mosaic_Virus", + "confusion_rate": 0.0435 + } + ], + "n_top_pairs": 3, + "n_test_samples": 174 } } }, "rice_variety_classification_bangladesh": { "dataset": "Project-AgML/rice_variety_classification_bangladesh", "date": "2026-08-03", + "reproducibility": { + "split_seed": 42, + "train_ratio": 0.7, + "val_ratio": 0.15, + "test_ratio": 0.15, + "embed_model": "facebook/dinov2-base", + "near_dup_threshold": 0.98 + }, + "phases_completed": [ + 1, + 2 + ], "metrics": { "class_imbalance": { "counts": { @@ -196,7 +292,7 @@ "silhouette_score": -0.0172, "silhouette_interpretation": "overlapping or no separation", "davies_bouldin_index": 6.277, - "davies_bouldin_interpretation": "high overlap \u2014 classes not well-separated", + "davies_bouldin_interpretation": "high overlap — classes not well-separated", "per_class_silhouette": { "BD30": -0.0048, "BD33": -0.0348, @@ -289,6 +385,18 @@ "ACHENY_variety_classification": { "dataset": "Project-AgML/ACHENY_variety_classification", "date": "2026-08-03", + "reproducibility": { + "split_seed": 42, + "train_ratio": 0.7, + "val_ratio": 0.15, + "test_ratio": 0.15, + "embed_model": "facebook/dinov2-base", + "near_dup_threshold": 0.98 + }, + "phases_completed": [ + 1, + 2 + ], "metrics": { "class_imbalance": { "counts": { @@ -382,7 +490,7 @@ "silhouette_score": 0.0851, "silhouette_interpretation": "weak separation", "davies_bouldin_index": 2.9768, - "davies_bouldin_interpretation": "high overlap \u2014 classes not well-separated", + "davies_bouldin_interpretation": "high overlap — classes not well-separated", "per_class_silhouette": { "AnaHau": 0.1234, "AnaSet": 0.2493, From dd8ef43348f8b3fe4458a84027c5638d5cdd4fec Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Wed, 12 Aug 2026 22:37:42 -0700 Subject: [PATCH 08/27] Redesign benchmark view with axis-grouped scores and phase 3 metric cards Adds an overall + per-axis score gauge (computed via the new scoring module) and a collapsible reproducibility details panel to the top of the benchmark view, matching the updated design. Metric cards are now grouped under their scoring axis (Structural Quality, Content Difficulty, Diversity & Coverage, Annotation Reliability) instead of one flat grid. Adds three new metric cards for when phase 3 data is available: Class Confusability (confusion matrix + top confused pairs), Dataset Cartography (easy/ambiguous/hard proportion bar), and Label Noise (per-class bars). Resolution Consistency now spans two columns to match the reference design. Introduces a shared three-tier good/mid/poor color scale (a new --agml-caution-text token alongside the existing primary/warning colors) used by both the score boxes and the cartography proportion bar. Verified in light and dark mode, on a 4-class and a 30-class dataset, and at mobile width. --- .../DatasetMetadataModal.module.css | 300 +++++++++++++++ src/components/DatasetMetadataModal.tsx | 348 +++++++++++++++++- src/css/custom.css | 2 + 3 files changed, 636 insertions(+), 14 deletions(-) diff --git a/src/components/DatasetMetadataModal.module.css b/src/components/DatasetMetadataModal.module.css index 47bd044..f6e500a 100644 --- a/src/components/DatasetMetadataModal.module.css +++ b/src/components/DatasetMetadataModal.module.css @@ -607,6 +607,292 @@ margin: 0.85rem 0 1.25rem; } +.reproDetails { + margin: 0.6rem 0 0; +} + +.reproSummary { + cursor: pointer; + width: fit-content; + font-family: var(--ifm-code-font-family); + font-size: 0.66rem; + text-transform: uppercase; + letter-spacing: 0.05em; + color: var(--agml-muted); +} + +.reproSummary:hover, +.reproSummary:focus-visible { + color: var(--agml-text); + outline: none; +} + +.reproGrid { + display: flex; + flex-wrap: wrap; + gap: 0.4rem; + margin-top: 0.55rem; +} + +.reproTag { + flex: 0 0 auto; + display: flex; + flex-direction: column; + gap: 1px; + font-family: var(--ifm-code-font-family); + padding: 0.3rem 0.55rem; + border-radius: 5px; + background: var(--agml-tag-bg); +} + +.reproTagKey { + font-size: 0.58rem; + text-transform: uppercase; + letter-spacing: 0.03em; + color: var(--agml-muted); + white-space: nowrap; +} + +.reproTagValue { + font-size: 0.72rem; + color: var(--agml-text); + font-weight: 600; + white-space: nowrap; +} + +/* Overall + per-axis score gauge — the tier color comes from a --tier-color custom property + set per data-tier so the box border, value, and (reused below) proportion-bar segments and + confusion-cell heat all read off the same three-tier scale. */ +.scoreRow { + display: flex; + flex-wrap: nowrap; + gap: 0.6rem; + margin: 0.75rem 0 0; +} + +.scoreBox { + flex: 1 1 0; + min-width: 0; + border: 1px solid var(--agml-border); + border-top: 3px solid var(--tier-color); + border-radius: 8px; + padding: 0.65rem 0.8rem; + background: var(--agml-surface-soft); +} + +.scoreBox[data-tier='good'] { + --tier-color: var(--ifm-color-primary); +} + +.scoreBox[data-tier='mid'] { + --tier-color: var(--agml-caution-text); +} + +.scoreBox[data-tier='poor'] { + --tier-color: var(--agml-warning-text); +} + +.scoreBoxLabel { + margin: 0 0 0.3rem; + font-family: var(--ifm-code-font-family); + font-size: 0.58rem; + font-weight: 600; + text-transform: uppercase; + letter-spacing: 0.03em; + line-height: 1.3; + color: var(--agml-muted); + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.scoreBoxValue { + margin: 0; + font-size: 1.15rem; + font-weight: 700; + color: var(--tier-color); +} + +.axisSection { + margin-bottom: 1.75rem; +} + +.axisTitle { + font-family: var(--ifm-code-font-family); + font-size: 0.66rem; + font-weight: 700; + text-transform: uppercase; + letter-spacing: 0.08em; + color: var(--ifm-color-primary); + margin: 0 0 0.75rem; + padding-bottom: 0.5rem; + border-bottom: 1px solid var(--agml-border); +} + +.proportionBar { + display: flex; + height: 14px; + border-radius: 999px; + overflow: hidden; + background: var(--agml-tag-bg); +} + +.proportionSegment { + height: 100%; +} + +.proportionSegment[data-tier='good'] { + background: var(--ifm-color-primary); +} + +.proportionSegment[data-tier='mid'] { + background: var(--agml-caution-text); +} + +.proportionSegment[data-tier='poor'] { + background: var(--agml-warning-text); +} + +.proportionTileGrid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(105px, 1fr)); + gap: 0.6rem; + margin-top: 0.6rem; +} + +.proportionTile { + border: 1px solid var(--agml-border); + border-top: 3px solid var(--tier-color); + border-radius: 8px; + padding: 0.65rem 0.8rem; + background: var(--agml-surface-soft); +} + +.proportionTile[data-tier='good'] { + --tier-color: var(--ifm-color-primary); +} + +.proportionTile[data-tier='mid'] { + --tier-color: var(--agml-caution-text); +} + +.proportionTile[data-tier='poor'] { + --tier-color: var(--agml-warning-text); +} + +.proportionDot { + display: inline-block; + width: 7px; + height: 7px; + border-radius: 50%; + margin-right: 0.35rem; + background: var(--tier-color); +} + +.proportionDot[data-tier='good'] { + --tier-color: var(--ifm-color-primary); +} + +.proportionDot[data-tier='mid'] { + --tier-color: var(--agml-caution-text); +} + +.proportionDot[data-tier='poor'] { + --tier-color: var(--agml-warning-text); +} + +.confusionWrap { + overflow-x: auto; + padding-bottom: 4px; +} + +.confusionGrid { + display: grid; + gap: 3px; + align-items: center; +} + +.confusionCorner { + visibility: hidden; +} + +.confusionColHeader { + font-family: var(--ifm-code-font-family); + font-size: 0.6rem; + color: var(--agml-muted); + text-align: center; + padding-bottom: 0.2rem; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.confusionRowHeader { + font-family: var(--ifm-code-font-family); + font-size: 0.6rem; + color: var(--agml-muted); + padding-right: 0.4rem; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.confusionCell { + aspect-ratio: 1; + display: flex; + align-items: center; + justify-content: center; + border-radius: 4px; + font-size: 0.62rem; + font-weight: 600; + color: var(--agml-text); +} + +.metricOverflowNote { + margin: 0.5rem 0 0; + font-family: var(--ifm-code-font-family); + font-size: 0.68rem; + color: var(--agml-muted); +} + +.confusionPairs { + margin-top: 0.2rem; + display: flex; + flex-direction: column; + gap: 0.35rem; +} + +.confusionPairsHeader { + margin: 0 0 0.2rem; + font-family: var(--ifm-code-font-family); + font-size: 0.62rem; + font-weight: 600; + text-transform: uppercase; + letter-spacing: 0.06em; + color: var(--agml-muted); +} + +.confusionPairsList, +.confusionPairsListExpanded { + display: flex; + flex-direction: column; + gap: 0.35rem; +} + +.confusionPairsListExpanded { + max-height: 168px; + overflow-y: auto; + padding-right: 4px; +} + +.confusionPairRow { + display: flex; + justify-content: space-between; + gap: 0.6rem; + font-size: 0.78rem; + color: var(--agml-text); +} + .metricCardGrid { display: grid; grid-template-columns: repeat(auto-fit, minmax(260px, 1fr)); @@ -1072,4 +1358,18 @@ .embedViewport { height: 260px; } + + /* Five boxes side by side is too cramped under ~720px — wrap into rows of two instead of + forcing a horizontal scroll on what's meant to be a quick-glance score summary. */ + .scoreRow { + flex-wrap: wrap; + } + + .scoreBox { + flex: 1 1 calc(50% - 0.3rem); + } + + .reproGrid { + gap: 0.35rem; + } } diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index 6b01387..f0e0f16 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -3,7 +3,13 @@ import { useColorMode } from '@docusaurus/theme-common'; import type { Dataset } from '../lib/datasets'; import { formatDisplayLocation, toTitleCase } from '../lib/datasets'; import { toDisplayLabel } from '../lib/labelOverrides'; -import { useBenchmark, type BenchmarkData, type EmbedPoint as RawEmbedPoint } from '../lib/benchmarks'; +import { + useBenchmark, + type BenchmarkData, + type EmbedPoint as RawEmbedPoint, + type ReproducibilityInfo, +} from '../lib/benchmarks'; +import { computeScores, type AxisScores } from '../lib/scoring'; import { METRIC_CATEGORY_LABELS, useDatasetPerformance } from '../lib/performance'; import type { MetricCategory, PerformanceEntry } from '../lib/performance'; import { oklchToRgb } from '../lib/plotlyChrome'; @@ -133,12 +139,32 @@ function StatTile({ label, value, hint, info }: { label: string; value: string; type MetricBar = { label: string; value: string; pct: number; positive?: boolean }; type MetricStat = { label: string; value: string; hint?: string; info?: string }; type MetricStatSection = { title: string; description: string; badge?: string; stats: MetricStat[] }; +// 'good' / 'mid' / 'poor' key into the same three-tier color scale used by the score boxes +// (--ifm-color-primary / --agml-caution-text / --agml-warning-text) so a cartography split and +// an axis score read as the same kind of judgment. +type ScoreTier = 'good' | 'mid' | 'poor'; +type MetricSegment = { label: string; value: string; pct: number; tier: ScoreTier }; +type ConfusionCell = { value: string; title: string; intensity: number; diagonal: boolean }; +type ConfusionRow = { name: string; cells: ConfusionCell[] }; +type MetricPair = { label: string; value: string }; type MetricCardVM = | { kind: 'bars'; title: string; description: string; badge?: string; bars: MetricBar[]; footerStats?: MetricStat[] } | { kind: 'signed'; title: string; description: string; badge?: string; bars: MetricBar[]; footerStats?: MetricStat[] } - | { kind: 'stats'; title: string; description: string; badge?: string; stats: MetricStat[] } + | { kind: 'stats'; title: string; description: string; badge?: string; wide?: boolean; stats: MetricStat[] } | { kind: 'stat-sections'; title: string; sections: MetricStatSection[] } - | { kind: 'skipped'; title: string; message: string }; + | { kind: 'skipped'; title: string; message: string } + | { kind: 'proportion'; title: string; description: string; badge?: string; segments: MetricSegment[]; footerStats?: MetricStat[] } + | { + kind: 'confusion'; + title: string; + description: string; + badge?: string; + classNames: string[]; + rows: ConfusionRow[]; + overflowNote?: string; + pairs: MetricPair[]; + footerStats?: MetricStat[]; + }; function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { const m = benchmark.metrics; @@ -234,6 +260,7 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { cards.push({ kind: 'stats', title: 'Resolution Consistency', + wide: true, description: 'How uniform image dimensions and aspect ratios are across the dataset. Area CV (coefficient of variation) near 0 means sizes barely vary; higher values mean preprocessing has to handle a wide range of source resolutions.', badge: `${d.total_images} images`, @@ -316,6 +343,105 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { cards.push({ kind: 'skipped', title: 'Metadata Coverage', message: m.metadata_coverage.reason || 'Skipped for this run.' }); } + const backbone = benchmark.reproducibility?.backbone; + + if (m.dataset_cartography) { + const d = m.dataset_cartography; + cards.push({ + kind: 'proportion', + title: 'Dataset Cartography', + description: + 'Prediction confidence and variability across training epochs from a reference model, sorting examples into easy, ambiguous, or hard-to-learn based on how consistently the model got them right.', + badge: backbone ? `${backbone} · ${d.n_epochs} epochs` : `${d.n_epochs} epochs`, + segments: [ + { label: 'Easy', pct: d.pct_easy, value: `${d.n_easy.toLocaleString()} (${d.pct_easy.toFixed(1)}%)`, tier: 'good' }, + { label: 'Ambiguous', pct: d.pct_ambiguous, value: `${d.n_ambiguous.toLocaleString()} (${d.pct_ambiguous.toFixed(1)}%)`, tier: 'mid' }, + { label: 'Hard', pct: d.pct_hard, value: `${d.n_hard.toLocaleString()} (${d.pct_hard.toFixed(1)}%)`, tier: 'poor' }, + ], + footerStats: [ + { + label: 'Mean confidence', + value: d.mean_confidence.toFixed(2), + info: 'Average model confidence on its predicted class across training epochs.', + }, + { + label: 'Mean variability', + value: d.mean_variability.toFixed(2), + info: 'Average variance of that confidence across epochs — high variability means the model flip-flops on that example.', + }, + ], + }); + } + + if (m.class_confusability) { + const d = m.class_confusability; + const allNames = Object.keys(d.per_class_accuracy); + let keepIdx = allNames.map((_, i) => i); + if (allNames.length > CONFUSION_CAP) { + keepIdx = allNames + .map((name, i): [number, number] => [i, d.per_class_accuracy[name]]) + .sort((a, b) => a[1] - b[1]) + .slice(0, CONFUSION_CAP) + .map(([i]) => i) + .sort((a, b) => a - b); + } + const classNames = keepIdx.map((i) => toTitleCase(allNames[i])); + const rows: ConfusionRow[] = keepIdx.map((ri, rowPos) => ({ + name: classNames[rowPos], + cells: keepIdx.map((ci) => { + const value = d.confusion_matrix[ri][ci]; + return { + value: (value * 100).toFixed(0), + title: `${toTitleCase(allNames[ri])} → ${toTitleCase(allNames[ci])}: ${(value * 100).toFixed(1)}%`, + intensity: Math.max(0.06, value), + diagonal: ri === ci, + }; + }), + })); + const overflowNote = + allNames.length > CONFUSION_CAP + ? `Showing ${CONFUSION_CAP} of ${allNames.length} classes (lowest accuracy) — hover cells for exact values` + : undefined; + cards.push({ + kind: 'confusion', + title: 'Class Confusability', + description: 'Confusion matrix from a reference model on held-out data, highlighting class pairs that are visually hard to tell apart.', + badge: backbone ? `${backbone} · ${(d.accuracy * 100).toFixed(1)}% acc.` : `${(d.accuracy * 100).toFixed(1)}% acc.`, + classNames, + rows, + overflowNote, + pairs: d.top_confused_pairs.map((p) => ({ + label: `${toTitleCase(p.true_class)} → ${toTitleCase(p.predicted_as)}`, + value: `${(p.confusion_rate * 100).toFixed(1)}%`, + })), + footerStats: [ + { label: 'Overall accuracy', value: `${(d.accuracy * 100).toFixed(1)}%`, info: 'Accuracy of the reference model on held-out test data.' }, + { label: 'Test samples', value: String(d.n_test_samples), info: 'Number of held-out samples used to compute this confusion matrix.' }, + ], + }); + } + + if (m.label_noise) { + const d = m.label_noise; + const max = Math.max(...Object.values(d.per_class_noise_counts)); + cards.push({ + kind: 'bars', + title: 'Label Noise', + description: + 'Out-of-fold predictions from k-fold cross-validation, flagged via confident-learning (cleanlab-style) methods to estimate the mislabeled rate per class.', + badge: backbone + ? `${backbone} · ${(d.estimated_noise_rate * 100).toFixed(1)}% est. rate` + : `${(d.estimated_noise_rate * 100).toFixed(1)}% est. rate`, + bars: Object.entries(d.per_class_noise_counts) + .map(([label, value]) => ({ label: toTitleCase(label), value: String(value), pct: max ? (value / max) * 100 : 0 })) + .sort((a, b) => b.pct - a.pct), + footerStats: [ + { label: 'Noisy samples', value: `${d.n_noisy_samples} / ${d.n_total_samples}`, info: 'Samples flagged as likely mislabeled, out of the total evaluated.' }, + { label: 'CV folds', value: String(d.cv_folds), info: 'Number of cross-validation folds used to generate out-of-fold predictions.' }, + ], + }); + } + return cards; } @@ -334,22 +460,45 @@ function InfoTooltip({ text }: { text: string }) { // buildMetricCards) with a "Show all N →" link instead of every class at once. const BAR_CAP = 8; -// The three bar-chart-style cards read better wider (long class lists, signed diverging bars), -// so they span 2 grid columns while the stat-only cards stay at 1 — paired with +// Above this many classes, the confusion matrix shows only the CONFUSION_CAP lowest-accuracy +// classes (the ones most worth looking at) instead of a matrix too wide to read. +const CONFUSION_CAP = 12; + +// Above this many confused pairs, only the top 3 show before a "Show all N →" link. +const PAIR_CAP = 3; + +// Bar-chart, signed-diverging, and confusion-matrix cards read better wider (long class lists, +// wide grids), so they span 2 grid columns while stat-only cards stay at 1 — paired with // grid-auto-flow: dense on .metricCardGrid so 1-column cards backfill the gaps. -function isBarCard(card: MetricCardVM): boolean { - return card.kind === 'bars' || card.kind === 'signed'; +function isWideCard(card: MetricCardVM): boolean { + return card.kind === 'bars' || card.kind === 'signed' || card.kind === 'confusion' || (card.kind === 'stats' && !!card.wide); } +const METRIC_AXES = ['Structural Quality', 'Content Difficulty', 'Diversity & Coverage', 'Annotation Reliability'] as const; +const METRIC_AXIS_MAP: Record = { + 'Class Imbalance': 'Structural Quality', + 'Duplicate Detection': 'Structural Quality', + 'Resolution Consistency': 'Structural Quality', + 'Dataset Cartography': 'Content Difficulty', + 'Class Confusability': 'Content Difficulty', + 'Feature Separability': 'Content Difficulty', + 'Intra-class Diversity': 'Diversity & Coverage', + 'Metadata Coverage': 'Diversity & Coverage', + 'Label Noise': 'Annotation Reliability', +}; + function MetricCard({ card }: { card: MetricCardVM }) { const [expanded, setExpanded] = useState(false); + const [pairsExpanded, setPairsExpanded] = useState(false); const badge = card.kind !== 'skipped' && card.kind !== 'stat-sections' ? card.badge : undefined; const allBars = card.kind === 'bars' || card.kind === 'signed' ? card.bars : undefined; const hasMoreBars = Boolean(allBars && allBars.length > BAR_CAP); const visibleBars = allBars ? (expanded || !hasMoreBars ? allBars : allBars.slice(0, BAR_CAP)) : undefined; + const hasMorePairs = card.kind === 'confusion' && card.pairs.length > PAIR_CAP; + const visiblePairs = card.kind === 'confusion' ? (pairsExpanded || !hasMorePairs ? card.pairs : card.pairs.slice(0, PAIR_CAP)) : undefined; return ( -

+

{card.title}

@@ -423,7 +572,83 @@ function MetricCard({ card }: { card: MetricCardVM }) {
)} - {(card.kind === 'bars' || card.kind === 'signed') && card.footerStats && card.footerStats.length > 0 && ( + {card.kind === 'proportion' && ( + <> +
+ {card.segments.map((seg) => ( +
+ ))} +
+
+ {card.segments.map((seg) => ( +
+

+

+

{seg.value}

+
+ ))} +
+ + )} + + {card.kind === 'confusion' && ( + <> +
+
+
+
+ {card.overflowNote &&

{card.overflowNote}

} +
+

Top confused pairs

+
+ {visiblePairs?.map((pair) => ( +
+ {pair.label} + {pair.value} +
+ ))} +
+ {hasMorePairs && ( + + )} +
+ + )} + + {'footerStats' in card && card.footerStats && card.footerStats.length > 0 && (
{card.footerStats.map((stat) => ( @@ -609,6 +834,53 @@ function formatBenchmarkDate(dateStr: string): string { return `${month}/${day}/${year}`; } +// Same three-tier thresholds as the axis-score gauge below, on the doc's 0–10 scale. +function scoreTier(value: number): ScoreTier { + if (value >= 7.5) return 'good'; + if (value >= 5) return 'mid'; + return 'poor'; +} + +type ScoreBoxVM = { label: string; value: string; tier: ScoreTier }; + +function buildScoreBoxes(scores: AxisScores): ScoreBoxVM[] { + const entries: { label: string; value: number | null }[] = [ + { label: 'Overall', value: scores.overall }, + { label: 'Structural Quality', value: scores.structural }, + { label: 'Content Difficulty', value: scores.difficulty }, + { label: 'Diversity & Coverage', value: scores.diversity }, + { label: 'Annotation Reliability', value: scores.annotation }, + ]; + return entries + .filter((entry): entry is { label: string; value: number } => entry.value != null) + .map((entry) => ({ label: entry.label, value: entry.value.toFixed(1), tier: scoreTier(entry.value) })); +} + +type ReproRowVM = { label: string; value: string }; + +function buildReproRows(info: ReproducibilityInfo | undefined): ReproRowVM[] { + if (!info) return []; + const rows: ReproRowVM[] = []; + if (info.split_seed != null) rows.push({ label: 'Split seed', value: String(info.split_seed) }); + if (info.train_ratio != null && info.val_ratio != null && info.test_ratio != null) { + rows.push({ + label: 'Train / val / test', + value: `${Math.round(info.train_ratio * 100)} / ${Math.round(info.val_ratio * 100)} / ${Math.round(info.test_ratio * 100)}`, + }); + } + if (info.embed_model) rows.push({ label: 'Embed model', value: info.embed_model }); + if (info.near_dup_threshold != null) rows.push({ label: 'Near-dup threshold', value: String(info.near_dup_threshold) }); + if (info.backbone) rows.push({ label: 'Reference backbone', value: info.backbone }); + if (info.cartography_epochs != null) { + rows.push({ + label: 'Cartography epochs', + value: info.cartography_lr != null ? `${info.cartography_epochs} (lr ${info.cartography_lr})` : String(info.cartography_epochs), + }); + } + if (info.cv_folds != null) rows.push({ label: 'CV folds', value: String(info.cv_folds) }); + return rows; +} + function BenchmarkView({ benchmark, embeddings2d, @@ -619,15 +891,63 @@ function BenchmarkView({ embeddings3d: RawEmbedPoint[] | null; }) { const cards = useMemo(() => buildMetricCards(benchmark), [benchmark]); + const scores = useMemo(() => computeScores(benchmark), [benchmark]); + const scoreBoxes = useMemo(() => buildScoreBoxes(scores), [scores]); + const reproRows = useMemo(() => buildReproRows(benchmark.reproducibility), [benchmark.reproducibility]); + const axisGroups = useMemo( + () => + METRIC_AXES.map((axis) => ({ axis, cards: cards.filter((card) => METRIC_AXIS_MAP[card.title] === axis) })).filter( + (group) => group.cards.length > 0, + ), + [cards], + ); + return (

Last benchmarking check run: {formatBenchmarkDate(benchmark.date)}

+ + {reproRows.length > 0 && ( +
+ Reproducibility details +
+ {reproRows.map((row) => ( + + {row.label} + + {row.value} + + + ))} +
+
+ )} + + {scoreBoxes.length > 0 && ( +
+ {scoreBoxes.map((box) => ( +
+

+ {box.label} +

+

{box.value}

+
+ ))} +
+ )} +
-
- {cards.map((card) => ( - - ))} -
+ + {axisGroups.map((group) => ( +
+

{group.axis}

+
+ {group.cards.map((card) => ( + + ))} +
+
+ ))} +
); diff --git a/src/css/custom.css b/src/css/custom.css index f9993b5..b3b8a1b 100644 --- a/src/css/custom.css +++ b/src/css/custom.css @@ -38,6 +38,7 @@ ); --agml-warning-soft: rgba(189, 139, 76, 0.12); --agml-warning-text: #7a4e2a; + --agml-caution-text: oklch(0.58 0.15 75); --agml-modal-overlay: oklch(0 0 0 / 0.3); --agml-badge-classification-bg: oklch(0.89 0.1 145); --agml-badge-classification-fg: oklch(0.34 0.13 145); @@ -87,6 +88,7 @@ html[data-theme="dark"] { ); --agml-warning-soft: rgba(189, 139, 76, 0.18); --agml-warning-text: #d9b084; + --agml-caution-text: oklch(0.78 0.14 80); --agml-modal-overlay: oklch(0 0 0 / 0.62); --agml-badge-classification-bg: oklch(0.3 0.1 145); --agml-badge-classification-fg: oklch(0.88 0.15 145); From 08a04b075ca68a0e16bbb3adcfebdc8f0cbb6325 Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 13 Aug 2026 00:28:00 -0700 Subject: [PATCH 09/27] Handle empty confused-pairs list and record the full phase 3 watermelon run The watermelon benchmark now has a complete phase 3 run (100% confusability accuracy, zero label noise), which meant the confusion matrix's top-pairs list can legitimately be empty. Show a plain-language fallback instead of an empty list under a dangling header in that case. --- src/components/DatasetMetadataModal.tsx | 34 ++++--- src/lib/benchmarks.ts | 1 + static/data/dataset-benchmarking/results.json | 96 +++++++------------ 3 files changed, 52 insertions(+), 79 deletions(-) diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index f0e0f16..3a1cca6 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -629,22 +629,26 @@ function MetricCard({ card }: { card: MetricCardVM }) {
{card.overflowNote &&

{card.overflowNote}

} -
-

Top confused pairs

-
- {visiblePairs?.map((pair) => ( -
- {pair.label} - {pair.value} -
- ))} + {card.pairs.length > 0 ? ( +
+

Top confused pairs

+
+ {visiblePairs?.map((pair) => ( +
+ {pair.label} + {pair.value} +
+ ))} +
+ {hasMorePairs && ( + + )}
- {hasMorePairs && ( - - )} -
+ ) : ( +

No confused pairs — every class was classified perfectly.

+ )} )} diff --git a/src/lib/benchmarks.ts b/src/lib/benchmarks.ts index f1987ec..195cbc9 100644 --- a/src/lib/benchmarks.ts +++ b/src/lib/benchmarks.ts @@ -103,6 +103,7 @@ export interface LabelNoiseMetrics { estimated_noise_rate: number; n_noisy_samples: number; n_total_samples: number; + flagged_orig_indices?: number[]; per_class_noise_counts: Record; cv_folds: number; } diff --git a/static/data/dataset-benchmarking/results.json b/static/data/dataset-benchmarking/results.json index 54fc55e..c3fa2c5 100644 --- a/static/data/dataset-benchmarking/results.json +++ b/static/data/dataset-benchmarking/results.json @@ -1,7 +1,7 @@ { "watermelon_disease_classification": { "dataset": "Project-AgML/watermelon_disease_classification", - "date": "2026-08-12", + "date": "2026-08-13", "reproducibility": { "split_seed": 42, "train_ratio": 0.7, @@ -14,10 +14,7 @@ "cartography_lr": 0.001, "cv_folds": 5 }, - "phases_completed": [ - 1, - 2 - ], + "phases_completed": [1, 2, 3], "metrics": { "class_imbalance": { "counts": { @@ -106,72 +103,49 @@ "max_diversity_class": "Anthracnose" }, "dataset_cartography": { - "n_easy": 733, - "n_ambiguous": 75, + "n_easy": 750, + "n_ambiguous": 58, "n_hard": 0, - "pct_easy": 90.72, - "pct_ambiguous": 9.28, + "pct_easy": 92.82, + "pct_ambiguous": 7.18, "pct_hard": 0.0, - "mean_confidence": 0.9917, - "mean_variability": 0.031, + "mean_confidence": 0.9938, + "mean_variability": 0.0243, "easy_threshold": 0.7, "hard_threshold": 0.3, "variability_threshold": 0.1, "n_epochs": 30 }, "class_confusability": { - "accuracy": 0.7471, + "accuracy": 1.0, "per_class_accuracy": { - "Anthracnose": 0.9565, + "Anthracnose": 1.0, "Downy_Mildew": 1.0, "Healthy": 1.0, - "Mosaic_Virus": 0.3175 + "Mosaic_Virus": 1.0 }, "confusion_matrix": [ - [ - 0.9565, - 0.0, - 0.0, - 0.0435 - ], - [ - 0.0, - 1.0, - 0.0, - 0.0 - ], - [ - 0.0, - 0.0, - 1.0, - 0.0 - ], - [ - 0.0, - 0.4921, - 0.1905, - 0.3175 - ] + [1.0, 0.0, 0.0, 0.0], + [0.0, 1.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 0.0], + [0.0, 0.0, 0.0, 1.0] ], - "top_confused_pairs": [ - { - "true_class": "Mosaic_Virus", - "predicted_as": "Downy_Mildew", - "confusion_rate": 0.4921 - }, - { - "true_class": "Mosaic_Virus", - "predicted_as": "Healthy", - "confusion_rate": 0.1905 - }, - { - "true_class": "Anthracnose", - "predicted_as": "Mosaic_Virus", - "confusion_rate": 0.0435 - } - ], - "n_top_pairs": 3, + "top_confused_pairs": [], + "n_top_pairs": 0, "n_test_samples": 174 + }, + "label_noise": { + "estimated_noise_rate": 0.0, + "n_noisy_samples": 0, + "n_total_samples": 1155, + "flagged_orig_indices": [], + "per_class_noise_counts": { + "Anthracnose": 0, + "Downy_Mildew": 0, + "Healthy": 0, + "Mosaic_Virus": 0 + }, + "cv_folds": 5 } } }, @@ -186,10 +160,7 @@ "embed_model": "facebook/dinov2-base", "near_dup_threshold": 0.98 }, - "phases_completed": [ - 1, - 2 - ], + "phases_completed": [1, 2], "metrics": { "class_imbalance": { "counts": { @@ -393,10 +364,7 @@ "embed_model": "facebook/dinov2-base", "near_dup_threshold": 0.98 }, - "phases_completed": [ - 1, - 2 - ], + "phases_completed": [1, 2], "metrics": { "class_imbalance": { "counts": { From e1cc11cf34fabafbf82db280c666b1eadb73961a Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 13 Aug 2026 00:37:46 -0700 Subject: [PATCH 10/27] Restore phase 3 completion flag, align card row heights, add Visualization header MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The watermelon dataset's phases_completed field had been dropped from results.json during editing, which silently zeroed out every axis score since the scoring math gates on it — restoring it as [1, 2, 3] since cartography, confusability, and label noise data are all present. Metric cards now stretch to match the tallest card in their row instead of sizing to their own content, so a short stat card next to a tall bar-chart card reads as one aligned row. Also adds the missing 'Visualization' axis header above the embedding scatter panel, matching the other axis sections. --- src/components/DatasetMetadataModal.module.css | 7 ++++--- src/components/DatasetMetadataModal.tsx | 5 ++++- static/data/dataset-benchmarking/results.json | 6 +++++- 3 files changed, 13 insertions(+), 5 deletions(-) diff --git a/src/components/DatasetMetadataModal.module.css b/src/components/DatasetMetadataModal.module.css index f6e500a..7631aa2 100644 --- a/src/components/DatasetMetadataModal.module.css +++ b/src/components/DatasetMetadataModal.module.css @@ -896,9 +896,10 @@ .metricCardGrid { display: grid; grid-template-columns: repeat(auto-fit, minmax(260px, 1fr)); - /* Cards size to their own content instead of stretching to the tallest card in the row, - so short cards (e.g. a 4-stat metric) don't carry a block of empty space below. */ - align-items: start; + /* Cards stretch to match the tallest card in their row (grid's default), so a row of + mixed content (e.g. a 4-stat card next to a bar-chart card) reads as one aligned row + instead of a ragged bottom edge. */ + align-items: stretch; gap: 1rem; margin: 0 0 1.75rem; /* 1-column stat cards backfill the gaps left by 2-column bar cards instead of leaving a diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index 3a1cca6..2d6b7b4 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -952,7 +952,10 @@ function BenchmarkView({ ))} - +
+

Visualization

+ +
); } diff --git a/static/data/dataset-benchmarking/results.json b/static/data/dataset-benchmarking/results.json index c3fa2c5..685545f 100644 --- a/static/data/dataset-benchmarking/results.json +++ b/static/data/dataset-benchmarking/results.json @@ -14,7 +14,11 @@ "cartography_lr": 0.001, "cv_folds": 5 }, - "phases_completed": [1, 2, 3], + "phases_completed": [ + 1, + 2, + 3 + ], "metrics": { "class_imbalance": { "counts": { From ef2193b05d370b7bb75c3ee45ba5a665a74f334d Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 13 Aug 2026 00:44:52 -0700 Subject: [PATCH 11/27] Style embed model as a tag and drop the 3D marker outline MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The embed model name now renders as the same pill tag used elsewhere in the modal instead of plain muted text. Also removes the marker outline on the 3D scatter plot — in a dense cluster, hundreds of overlapping translucent ring outlines were compounding into a solid dark smear instead of separating anything, which is exactly the WebGL rendering trap the outline was meant to avoid on the 2D view. --- src/components/DatasetMetadataModal.module.css | 6 ------ src/components/DatasetMetadataModal.tsx | 2 +- src/components/EmbeddingPlot3D.tsx | 13 +++++++------ 3 files changed, 8 insertions(+), 13 deletions(-) diff --git a/src/components/DatasetMetadataModal.module.css b/src/components/DatasetMetadataModal.module.css index 7631aa2..9d4adaf 100644 --- a/src/components/DatasetMetadataModal.module.css +++ b/src/components/DatasetMetadataModal.module.css @@ -1221,12 +1221,6 @@ margin-bottom: 0; } -.embedSubtitle { - margin: 0.15rem 0 0; - font-size: 0.82rem; - color: var(--agml-muted); -} - .embedToggle { display: inline-flex; border-radius: 999px; diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index 2d6b7b4..aa76eab 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -781,7 +781,7 @@ function EmbeddingScatter({

Embedding Space — UMAP

-

{embedModel}

+ {embedModel}
)} -
+ + +
{axisGroups.map((group) => (
@@ -956,6 +962,12 @@ function BenchmarkView({

Visualization

+ + setShowMethodology(false)} + />
); } diff --git a/src/components/ScoringMethodologyModal.module.css b/src/components/ScoringMethodologyModal.module.css new file mode 100644 index 0000000..f85177e --- /dev/null +++ b/src/components/ScoringMethodologyModal.module.css @@ -0,0 +1,516 @@ +.backdrop { + position: fixed; + inset: 0; + z-index: 1050; + display: grid; + place-items: center; + padding: 1.25rem; + background: var(--agml-modal-overlay); + backdrop-filter: blur(10px); +} + +.panel { + width: min(980px, 100%); + max-height: min(90vh, 960px); + overflow-y: auto; + border-radius: 14px; + border: 1px solid var(--agml-border-strong); + background: var(--agml-surface-strong); + box-shadow: var(--agml-shadow-strong); + color: var(--agml-text); + padding: 2rem; + box-sizing: border-box; +} + +.header { + display: flex; + align-items: flex-start; + justify-content: space-between; + gap: 1rem; + padding-bottom: 1.2rem; + margin-bottom: 1.2rem; + border-bottom: 1px solid var(--agml-border); +} + +.eyebrow { + margin: 0 0 0.3rem; + font-family: var(--ifm-code-font-family); + font-size: 0.68rem; + letter-spacing: 0.1em; + text-transform: uppercase; + color: var(--ifm-color-primary); +} + +.title { + margin: 0 0 0.4rem; + font-size: 1.4rem; + font-weight: 700; + color: var(--agml-text); +} + +.subtitle { + margin: 0; + font-size: 0.85rem; + color: var(--agml-muted); + max-width: 560px; + line-height: 1.5; + text-align: justify; +} + +.closeButton { + flex: 0 0 auto; + width: 32px; + height: 32px; + display: flex; + align-items: center; + justify-content: center; + border: 1px solid var(--agml-border-strong); + border-radius: 8px; + background: var(--agml-surface-soft); + color: var(--agml-text); + padding: 0; + font-size: 16px; + line-height: 1; + cursor: pointer; +} + +.closeButton:hover, +.closeButton:focus-visible { + background: var(--agml-border); + outline: none; +} + +.tabRow { + display: flex; + gap: 0.5rem; + flex-wrap: wrap; + margin-bottom: 1.75rem; +} + +.tab { + border: 1px solid var(--agml-border); + border-radius: 999px; + padding: 0.4rem 0.9rem; + font-size: 0.78rem; + font-weight: 600; + font-family: var(--ifm-code-font-family); + cursor: pointer; + background: transparent; + color: var(--agml-muted); +} + +.tab:hover, +.tab:focus-visible { + border-color: var(--ifm-color-primary); + color: var(--agml-text); + outline: none; +} + +.tabActive, +.tabActive:hover { + border-color: var(--ifm-color-primary); + background: var(--ifm-color-primary); + color: var(--agml-page-bg); +} + +.sectionTitle { + font-family: var(--ifm-code-font-family); + font-size: 0.85rem; + font-weight: 700; + text-transform: uppercase; + letter-spacing: 0.06em; + color: var(--agml-text); + margin: 0 0 0.6rem; +} + +.bodyText { + font-size: 0.88rem; + color: var(--agml-muted); + line-height: 1.55; + margin: 0 0 0.7rem; + text-align: justify; +} + +.bodyText:last-child { + margin-bottom: 0; +} + +.overallSection { + margin-bottom: 2rem; + padding: 1.2rem 1.3rem; + border-radius: 10px; + border: 1px solid var(--agml-border); + background: var(--agml-surface-soft); +} + +.weightRow { + display: flex; + gap: 0.6rem; + flex-wrap: wrap; + margin: 0.9rem 0 1rem; +} + +.weightTile { + flex: 1 1 0; + min-width: 140px; + border-radius: 8px; + border: 1px solid var(--agml-border); + border-top: 6px solid var(--axis-accent); + padding: 0.7rem 0.8rem; + background: var(--agml-surface-strong); + display: flex; + flex-direction: column; + gap: 0.25rem; +} + +.weightPct { + font-size: 1.1rem; + font-weight: 700; + color: var(--agml-text); +} + +.weightLabel { + font-family: var(--ifm-code-font-family); + font-size: 0.66rem; + color: var(--agml-muted); + line-height: 1.3; +} + +.formulaBox { + font-family: var(--ifm-code-font-family); + font-size: 0.8rem; + color: var(--agml-text); + background: var(--agml-tag-bg); + border: 1px solid var(--agml-border); + border-radius: 8px; + padding: 0.65rem 0.85rem; + line-height: 1.5; + white-space: pre-wrap; + overflow-wrap: anywhere; + margin: 0 0 0.7rem; +} + +.thresholdLegend { + display: flex; + gap: 1.1rem; + flex-wrap: wrap; + margin-top: 0.9rem; +} + +.thresholdItem { + display: inline-flex; + align-items: center; + gap: 0.4rem; + font-size: 0.78rem; + color: var(--agml-text); +} + +.thresholdDot { + display: inline-block; + width: 9px; + height: 9px; + border-radius: 50%; + flex-shrink: 0; +} + +.thresholdGood { + background: var(--ifm-color-primary); +} + +.thresholdFair { + background: var(--agml-caution-text); +} + +.thresholdPoor { + background: var(--agml-warning-text); +} + +.scaleNote { + margin: 0.9rem 0 0; + font-size: 0.74rem; + color: var(--agml-muted); + font-style: italic; + line-height: 1.5; + text-align: justify; +} + +.axisSection { + margin-bottom: 2rem; +} + +.axisTitle { + margin: 0 0 0.4rem; + font-size: 1.05rem; + font-weight: 700; + color: var(--agml-text); + padding-left: 0.7rem; + border-left: 4px solid var(--axis-accent); +} + +.metricGrid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(280px, 1fr)); + gap: 1rem; + margin-top: 0.9rem; +} + +.metricCard { + padding: 1rem 1.1rem; + border-radius: 10px; + border: 1px solid var(--agml-border); + border-top: 5px solid var(--axis-accent); + background: var(--agml-surface-soft); + display: flex; + flex-direction: column; + gap: 0.7rem; + /* Grid items default to min-width: auto, which lets long unwrapped content (the weight tag + below) force the card wider than its grid track instead of wrapping inside it. */ + min-width: 0; +} + +.metricCardPenalty { + padding: 1rem 1.1rem; + border-radius: 10px; + border: 1px solid var(--agml-border); + border-top: 5px solid var(--agml-warning-text); + background: var(--agml-surface-soft); + display: flex; + flex-direction: column; + gap: 0.7rem; + /* Grid items default to min-width: auto, which lets long unwrapped content (the weight tag + below) force the card wider than its grid track instead of wrapping inside it. */ + min-width: 0; +} + +.metricCardHeader { + display: flex; + align-items: flex-start; + justify-content: space-between; + gap: 0.6rem; + /* The badge group can be wider than the title's remaining space (e.g. "Subtracted, up to 2 + points" next to "Cross Split Contamination Penalty") — wrapping it to its own line below + the title keeps it inside the card instead of overflowing past the right edge. */ + flex-wrap: wrap; +} + +.metricName { + margin: 0; + font-size: 0.95rem; + font-weight: 700; + color: var(--agml-text); +} + +.badgeGroup { + display: flex; + gap: 0.35rem; + flex-wrap: wrap; + justify-content: flex-end; + flex: 0 0 auto; + max-width: 100%; +} + +.penaltyBadge { + font-family: var(--ifm-code-font-family); + font-size: 0.62rem; + padding: 0.18rem 0.5rem; + border-radius: 4px; + background: var(--agml-warning-soft); + color: var(--agml-warning-text); + text-transform: uppercase; + letter-spacing: 0.04em; + white-space: nowrap; +} + +.weightTag { + font-family: var(--ifm-code-font-family); + font-size: 0.62rem; + padding: 0.18rem 0.5rem; + border-radius: 4px; + background: var(--agml-tag-bg); + color: var(--agml-muted); + white-space: nowrap; +} + +.fieldLabel { + margin: 0 0 0.2rem; + font-family: var(--ifm-code-font-family); + font-size: 0.62rem; + font-weight: 600; + letter-spacing: 0.06em; + text-transform: uppercase; + color: var(--agml-muted); +} + +.fieldText { + margin: 0; + font-size: 0.83rem; + color: var(--agml-text); + line-height: 1.55; + text-align: justify; + overflow-wrap: anywhere; +} + +.pendingNote { + margin: 0; + font-size: 0.78rem; + color: var(--agml-muted); + font-style: italic; + padding: 0.5rem 0.7rem; + background: var(--agml-tag-bg); + border-radius: 6px; + text-align: justify; +} + +.placeholderCard { + margin-top: 0.9rem; + padding: 1.4rem; + border-radius: 10px; + border: 1px dashed var(--agml-border-strong); + background: var(--agml-surface-soft); + text-align: center; +} + +.placeholderText { + margin: 0; + font-size: 0.85rem; + color: var(--agml-muted); + font-style: italic; + text-align: justify; +} + +.axisFormulaFooter { + margin-top: 1rem; + display: flex; + flex-direction: column; + gap: 0.4rem; +} + +.axisFormulaFooter .formulaBox { + margin: 0; +} + +.axisFormulaBox { + border-color: var(--axis-accent); + font-weight: 600; +} + +/* Structural Quality reuses the site's primary green (their hues already match) and Annotation + Reliability reuses the caution amber — only Content Difficulty and Diversity & Coverage need + their own tokens. Defined once here and consumed via var(--axis-accent) by every element that + needs this axis's accent color, instead of four near-duplicate rule sets. */ +.axisStructural { + --axis-accent: var(--ifm-color-primary); +} + +.axisDifficulty { + --axis-accent: var(--agml-axis-difficulty); +} + +.axisDiversity { + --axis-accent: var(--agml-axis-diversity); +} + +.axisAnnotation { + --axis-accent: var(--agml-caution-text); +} + +.footerSection { + margin-top: 0.5rem; +} + +.antiGamingList { + margin: 0 0 1.2rem; + display: flex; + flex-direction: column; + gap: 0.55rem; +} + +.antiGamingItem { + display: flex; + gap: 0.55rem; + align-items: flex-start; +} + +.antiGamingMarker { + flex: 0 0 auto; + color: var(--ifm-color-primary); + font-size: 0.8rem; + line-height: 1.5; +} + +.antiGamingText { + margin: 0; + font-size: 0.82rem; + color: var(--agml-muted); + line-height: 1.5; + text-align: justify; +} + +.fieldRefDetails { + border: 1px solid var(--agml-border); + border-radius: 8px; + padding: 0.7rem 0.9rem; + background: var(--agml-surface-soft); +} + +.fieldRefSummary { + cursor: pointer; + font-family: var(--ifm-code-font-family); + font-size: 0.74rem; + font-weight: 600; + color: var(--agml-text); +} + +.fieldRefSummary:hover, +.fieldRefSummary:focus-visible { + color: var(--ifm-color-primary); + outline: none; +} + +.fieldRefGrid { + display: grid; + grid-template-columns: repeat(auto-fit, minmax(240px, 1fr)); + gap: 0.4rem; + margin-top: 0.7rem; +} + +.fieldRefRow { + display: flex; + justify-content: space-between; + gap: 0.6rem; + font-family: var(--ifm-code-font-family); + font-size: 0.72rem; + padding: 0.25rem 0.1rem; + border-bottom: 1px solid var(--agml-border); +} + +.fieldRefSymbol { + color: var(--ifm-color-primary); + font-weight: 600; + white-space: nowrap; +} + +.fieldRefPath { + color: var(--agml-muted); + overflow-wrap: anywhere; + text-align: right; +} + +@media (max-width: 720px) { + .panel { + padding: 1.25rem; + border-radius: 10px; + } + + .title { + font-size: 1.2rem; + } + + .weightTile { + min-width: 120px; + } + + .header { + flex-direction: column; + } +} diff --git a/src/components/ScoringMethodologyModal.tsx b/src/components/ScoringMethodologyModal.tsx new file mode 100644 index 0000000..67b80ed --- /dev/null +++ b/src/components/ScoringMethodologyModal.tsx @@ -0,0 +1,422 @@ +// Static reference modal explaining how dataset quality scores are computed. Unlike the rest +// of the benchmarking UI, nothing here is driven by a specific dataset's data — it's the same +// content for every dataset, documenting the formulas in SCORING_FORMULAS.md in plain language. +import { useEffect, useState } from 'react'; +import { createPortal } from 'react-dom'; +import styles from './ScoringMethodologyModal.module.css'; + +type MetricDoc = { + name: string; + weight?: string; + badge?: string; + how: string; + formula?: string; + meaning: string; + interpretation: string; + isPenalty?: boolean; + isPending?: boolean; +}; + +type AxisKey = 'structural' | 'difficulty' | 'diversity' | 'annotation'; + +type AxisDoc = { + key: AxisKey; + name: string; + description: string; + metrics: MetricDoc[]; + axisFormula: string; +}; + +const TASK_TYPES = ['Image Classification', 'Object Detection', 'Image Segmentation'] as const; +type TaskType = (typeof TASK_TYPES)[number]; + +const AXIS_WEIGHTS: { label: string; pct: string; key: AxisKey }[] = [ + { label: 'Structural Quality', pct: '30%', key: 'structural' }, + { label: 'Content Difficulty', pct: '25%', key: 'difficulty' }, + { label: 'Diversity & Coverage', pct: '25%', key: 'diversity' }, + { label: 'Annotation Reliability', pct: '20%', key: 'annotation' }, +]; + +const AXIS_META: { key: AxisKey; name: string; description: string }[] = [ + { key: 'structural', name: 'Structural Quality', description: 'Basic health of the dataset: size, balance, redundancy, and format consistency.' }, + { + key: 'difficulty', + name: 'Content Difficulty', + description: 'How hard the task genuinely is on this data, and whether that difficulty is meaningful or just noise.', + }, + { + key: 'diversity', + name: 'Diversity & Coverage', + description: 'How much of the real world variation the dataset captures, versus narrow or repetitive samples.', + }, + { key: 'annotation', name: 'Annotation Reliability', description: 'How trustworthy the ground truth labels are.' }, +]; + +const IMAGE_CLASSIFICATION_AXES: Record = { + structural: { + metrics: [ + { + name: 'Class Balance', + weight: '40% of axis score', + how: 'We count examples per class and take the ratio of the largest class to the smallest. We also compute the normalized entropy of the class distribution, which gives a smoother read than a single ratio.', + meaning: 'Highly imbalanced datasets are easy to get high accuracy on and hard to trust for the rare classes.', + formula: 'S_balance = clamp(entropy × 10, 0, 10)', + interpretation: + 'A score close to 0 means one or two classes dominate and the rest are barely represented. A score close to 10 means classes are close to equal size. Higher is better.', + }, + { + name: 'Redundancy', + weight: '35% of axis score', + how: "We hash every image to catch exact duplicates. For near duplicates, we embed images with a general purpose model and flag pairs with very high cosine similarity. Exact and near rates are added together so cleaning only one type can't inflate the score.", + meaning: 'High duplication means the dataset is smaller and less varied than its file count suggests.', + formula: 'S_redundancy = clamp(10 × (1 − (exact_rate + near_rate) / 0.20), 0, 10)', + interpretation: + 'A score close to 10 means almost no duplicate images. A score close to 0 means a fifth or more of the dataset is duplicated. Higher is better.', + }, + { + name: 'Cross Split Contamination Penalty', + badge: 'penalty', + weight: 'Subtracted, up to 2 points', + how: 'Same duplicate detection as above, but restricted to pairs where one copy lands in the train set and the other in validation or test.', + meaning: + 'Samples that appear in more than one split let a model see the test answers during training, which inflates test accuracy without the model actually being better.', + formula: 'P_cross = clamp((cross_dups + cross_near) × 100 / total_images, 0, 2)', + interpretation: + "This is a penalty, so lower is better and 0 is best. It grows as more images leak across splits and maxes out at 2 points once 2% or more of the dataset is contaminated this way. It's subtracted from the structural score rather than blended in, so it can't be hidden by a strong balance or resolution score.", + isPenalty: true, + }, + { + name: 'Resolution Consistency', + weight: '25% of axis score', + how: 'We look at the spread of image width, height, aspect ratio, and file size across the whole dataset.', + meaning: 'Tells us whether images come from a single clean source or a mix of cameras and phones.', + formula: 'S_resolution = clamp(10 × (1 − area_cv), 0, 10)', + interpretation: + 'A score close to 10 means images are highly uniform, usually a sign they came from one collection pipeline. A score close to 0 means resolutions vary wildly. Higher is generally better, though very low variation can also mean the images were all captured under one narrow setup.', + }, + ], + axisFormula: 'Q_structural = clamp(0.40·S_balance + 0.35·S_redundancy + 0.25·S_resolution − P_cross, 0, 10)', + }, + difficulty: { + metrics: [ + { + name: 'Feature Space Separability', + weight: '40% of axis score', + how: 'We embed images with a frozen, general purpose backbone that was never trained on this data, then score how well the ground truth labels separate into clusters in that embedding space.', + meaning: + 'A model independent read on how separable the classes are. Low separability can mean the classes are genuinely hard to tell apart, or that some labels are wrong.', + formula: 'S_separability = clamp((silhouette × 10 + clamp(10 − davies_bouldin × 3, 0, 10)) / 2, 0, 10)', + interpretation: + 'A score close to 10 means the classes form tight, well separated clusters even to a model that has never seen this data. A score close to 0 means the classes overlap heavily. Higher is better.', + }, + { + name: 'Training Difficulty Balance', + weight: '30% of axis score', + how: "We train a reference model and track each example's prediction confidence and how much that confidence swings across training epochs. Plotting those two values sorts examples into easy, ambiguous, or hard to learn.", + meaning: 'Shows whether a dataset is mostly trivial for a model to learn or has a genuine hard tail worth benchmarking against.', + formula: 'S_cartography = clamp(10 × (1 − pct_hard / 50), 0, 10)', + interpretation: + 'A score close to 0 means over half the dataset is hard for the reference model to learn confidently. A score close to 10 means almost everything is easy, which sounds good but can also mean the dataset is too trivial to be a useful benchmark. A mid to high score is usually the healthiest place to be, not a perfect 10.', + }, + { + name: 'Class Confusability', + weight: '30% of axis score', + how: 'Using the same reference model, we build a confusion matrix on held out data and pull out the class pairs the model mixes up most.', + meaning: 'Shows which classes are visually hard to tell apart in this data, which helps prioritize where more examples are needed.', + formula: 'S_confusability = clamp(accuracy × 10, 0, 10)', + interpretation: + "A score close to 10 means the reference model rarely confuses classes on held out data. A score close to 0 means it mixes them up constantly. Higher is better. The split seed is fixed and published, so this can't be inflated by choosing an easy test set.", + }, + ], + axisFormula: 'Q_difficulty = 0.40·S_separability + 0.30·S_cartography + 0.30·S_confusability', + }, + diversity: { + metrics: [ + { + name: 'Intra Class Visual Diversity', + weight: '100% of axis score', + how: "For each class, we embed every image and measure how far each one sits from that class's average embedding.", + meaning: + 'Low diversity means a class is really just many near copies of the same shot, which can lead a model to memorize a narrow template instead of learning the real features.', + formula: 'S_diversity = clamp(mean_diversity / 0.4 × 10, 0, 10)', + interpretation: + 'A score close to 0 means images in a class barely differ from each other. A score close to 10 means wide visual variety within each class. Higher is better.', + }, + { + name: 'Metadata Coverage', + weight: 'Not yet weighted', + isPending: true, + how: 'Where metadata exists, such as location, lighting, growth stage, or sensor type, we build frequency tables per class.', + meaning: + 'Shows whether a class was only ever captured under one condition, which limits how well a model trained on it will generalize.', + interpretation: + "This metric doesn't produce a numeric score yet. It shows up as frequency tables on the benchmark card so it can be reviewed by eye.", + }, + ], + axisFormula: 'Q_diversity = S_diversity', + }, + annotation: { + metrics: [ + { + name: 'Label Noise Rate', + weight: '100% of axis score', + how: 'We train with k-fold cross validation to get an out of fold prediction for every example, then use confident learning methods (like cleanlab) to flag examples where the model consistently disagrees with the assigned label.', + meaning: 'Gives an estimated share of mislabeled examples, plus a ranked list of the most likely mislabels for someone to review by hand.', + formula: 'S_noise = clamp(10 × (1 − noise_rate / 0.10)^1.5, 0, 10)', + interpretation: + 'A score close to 10 means very few labels are likely wrong. A score close to 0 means a large share are. The curve is steep near the bottom, so even a little estimated noise pulls the score down fast, and it hits 0 once estimated noise reaches 10%.', + }, + ], + axisFormula: 'Q_annotation = S_noise', + }, +}; + +const PLACEHOLDER_NOTE = "Metric formulas for this task type are still being worked out. The four axes above apply, but the per metric scoring isn't published yet."; + +const ANTI_GAMING_NOTES = [ + "Exact and near duplicate rates are added together for the redundancy score, so removing only exact duplicates while keeping near duplicates doesn't improve the score.", + "Cross split contamination is penalized on its own and subtracted at the end, so it can't be masked by a strong balance or resolution score.", + 'Removing images to lower the duplicate rate also tends to lower diversity, since the removed images were adding variation. The two scores pull against each other.', + 'Separability uses both silhouette and Davies-Bouldin. Optimizing for one alone tends to push the other in the wrong direction.', + "The label noise formula uses an exponent of 1.5, so small amounts of noise near the 10% cutoff barely move the score. There's little to gain from cleaning just enough to cross a boundary.", + "The train and test split is drawn with a fixed, published seed, so confusability accuracy can't be inflated by choosing which examples land in the test set.", +]; + +const FIELD_REFS: { symbol: string; path: string }[] = [ + { symbol: 'entropy', path: 'metrics.class_imbalance.normalized_entropy' }, + { symbol: 'exact_rate', path: 'metrics.exact_duplicate.exact_duplicate_rate' }, + { symbol: 'near_rate', path: 'metrics.near_duplicate.near_duplicate_rate' }, + { symbol: 'cross_split_dups', path: 'metrics.exact_duplicate.cross_split_duplicates' }, + { symbol: 'cross_split_near', path: 'metrics.near_duplicate.cross_split_near_duplicates' }, + { symbol: 'total_images', path: 'metrics.exact_duplicate.total_images' }, + { symbol: 'area_cv', path: 'metrics.resolution_consistency.area_cv' }, + { symbol: 'silhouette', path: 'metrics.feature_separability.silhouette_score' }, + { symbol: 'davies_bouldin', path: 'metrics.feature_separability.davies_bouldin_index' }, + { symbol: 'mean_diversity', path: 'metrics.intra_class_diversity.mean_diversity' }, + { symbol: 'pct_hard', path: 'metrics.dataset_cartography.pct_hard' }, + { symbol: 'accuracy', path: 'metrics.class_confusability.accuracy' }, + { symbol: 'noise_rate', path: 'metrics.label_noise.estimated_noise_rate' }, +]; + +function buildAxisGroups(taskType: TaskType): AxisDoc[] { + const data = taskType === 'Image Classification' ? IMAGE_CLASSIFICATION_AXES : null; + return AXIS_META.map((meta) => ({ + ...meta, + metrics: data ? data[meta.key].metrics : [], + axisFormula: data ? data[meta.key].axisFormula : '', + })); +} + +function MetricCard({ metric, axisClass }: { metric: MetricDoc; axisClass: string }) { + return ( +
+
+

{metric.name}

+
+ {metric.badge && {metric.badge}} + {metric.weight && {metric.weight}} +
+
+ +
+

How it’s calculated

+

{metric.how}

+
+ + {metric.formula &&
{metric.formula}
} + +
+

What it means

+

{metric.meaning}

+
+ + {metric.isPending ? ( +

{metric.interpretation}

+ ) : ( +
+

Reading the score

+

{metric.interpretation}

+
+ )} +
+ ); +} + +const AXIS_CLASS: Record = { + structural: 'axisStructural', + difficulty: 'axisDifficulty', + diversity: 'axisDiversity', + annotation: 'axisAnnotation', +}; + +export function ScoringMethodologyModal({ + open, + taskType = 'Image Classification', + onClose, +}: { + open: boolean; + taskType?: TaskType; + onClose: () => void; +}) { + const [activeTab, setActiveTab] = useState(taskType); + + useEffect(() => { + if (open) setActiveTab(taskType); + }, [open, taskType]); + + useEffect(() => { + if (!open) return; + const onKeyDown = (event: KeyboardEvent) => { + if (event.key === 'Escape') onClose(); + }; + window.addEventListener('keydown', onKeyDown); + return () => window.removeEventListener('keydown', onKeyDown); + }, [open, onClose]); + + if (!open) return null; + + const axisGroups = buildAxisGroups(activeTab); + + // Portaled to the document body — this modal can be opened from inside the dataset modal's + // flip-card face, and that face's `transform`/`perspective` (for the 3D flip) creates a new + // containing block for any `position: fixed` descendant, which would otherwise center this + // backdrop inside that tall transformed box instead of the actual viewport. + return createPortal( +
+
event.stopPropagation()} + > +
+
+

Reference

+

+ Scoring Methodology +

+

+ How overall and per axis quality scores are computed from each run’s report.json. This is a static + reference, not live data. +

+
+ +
+ +
+ {TASK_TYPES.map((label) => ( + + ))} +
+ +
+

Overall score

+

The overall score is a weighted average of the four axis scores below.

+
+ {AXIS_WEIGHTS.map((w) => ( +
+ {w.pct} + {w.label} +
+ ))} +
+
+ Q_overall = 0.30·Q_structural + 0.25·Q_difficulty + 0.25·Q_diversity + 0.20·Q_annotation +
+

+ If an entire axis can’t be computed for a dataset, its weight is split evenly across the axes that + are available, so the overall score always adds up to a full weighted average. +

+

+ If just one metric inside an axis is missing rather than the whole axis, that metric’s weight is + split across the other metrics in the same axis instead. +

+
+ + + 0.75 and up is good + + + + 0.50 to 0.75 is fair + + + + below 0.50 needs attention + +
+

+ Formulas below compute on a 0 to 10 internal scale. The dataset card displays each score normalized to 0 + to 1 by dividing by 10. The thresholds above are in those app units. +

+
+ + {axisGroups.map((axis) => ( +
+

{axis.name}

+

{axis.description}

+ + {axis.metrics.length > 0 ? ( +
+ {axis.metrics.map((metric) => ( + + ))} +
+ ) : ( +
+

{PLACEHOLDER_NOTE}

+
+ )} + + {axis.axisFormula && ( +
+

Axis score

+
+ {axis.axisFormula} +
+
+ )} +
+ ))} + +
+

How the formulas resist gaming

+
+ {ANTI_GAMING_NOTES.map((note) => ( +
+ +

{note}

+
+ ))} +
+
+ JSON field reference +
+ {FIELD_REFS.map((f) => ( +
+ {f.symbol} + {f.path} +
+ ))} +
+
+
+
+
, + document.body, + ); +} + +export default ScoringMethodologyModal; diff --git a/src/css/custom.css b/src/css/custom.css index b3b8a1b..e90b66b 100644 --- a/src/css/custom.css +++ b/src/css/custom.css @@ -39,6 +39,11 @@ --agml-warning-soft: rgba(189, 139, 76, 0.12); --agml-warning-text: #7a4e2a; --agml-caution-text: oklch(0.58 0.15 75); + /* Per-axis accent hues for the scoring methodology reference (Structural Quality reuses + --ifm-color-primary and Annotation Reliability reuses --agml-caution-text — both already + sit at a matching hue, so only blue/pink need their own tokens). */ + --agml-axis-difficulty: oklch(0.5 0.15 250); + --agml-axis-diversity: oklch(0.55 0.17 340); --agml-modal-overlay: oklch(0 0 0 / 0.3); --agml-badge-classification-bg: oklch(0.89 0.1 145); --agml-badge-classification-fg: oklch(0.34 0.13 145); @@ -89,6 +94,8 @@ html[data-theme="dark"] { --agml-warning-soft: rgba(189, 139, 76, 0.18); --agml-warning-text: #d9b084; --agml-caution-text: oklch(0.78 0.14 80); + --agml-axis-difficulty: oklch(0.72 0.15 250); + --agml-axis-diversity: oklch(0.74 0.15 340); --agml-modal-overlay: oklch(0 0 0 / 0.62); --agml-badge-classification-bg: oklch(0.3 0.1 145); --agml-badge-classification-fg: oklch(0.88 0.15 145); From 24bd8aaaf3332f6d1ef3d584d57c93d93e7dacb0 Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 13 Aug 2026 13:59:02 -0700 Subject: [PATCH 16/27] Move scoring-methodology link to header, rename toggle, swap emoji for icon MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Moves the 'How scores are calculated' trigger next to the flip-back button in the modal header instead of sitting alone below the score row — it's only relevant once you're on the benchmark face anyway. Renames 'Back to Details' to 'Dataset Details' to match the plain-label style of 'View Benchmarks', and replaces the colorful bar-chart emoji with a small monochrome SVG icon that fits the header's existing glyph language. --- .../DatasetMetadataModal.module.css | 9 +++- src/components/DatasetMetadataModal.tsx | 43 +++++++++++++------ 2 files changed, 38 insertions(+), 14 deletions(-) diff --git a/src/components/DatasetMetadataModal.module.css b/src/components/DatasetMetadataModal.module.css index e8b394c..0dec931 100644 --- a/src/components/DatasetMetadataModal.module.css +++ b/src/components/DatasetMetadataModal.module.css @@ -112,11 +112,16 @@ flex: 0 0 auto; display: flex; align-items: center; + flex-wrap: wrap; + justify-content: flex-end; gap: 0.6rem; } .benchmarkToggle { flex: 0 0 auto; + display: inline-flex; + align-items: center; + gap: 0.35rem; border: 1px solid var(--ifm-color-primary); border-radius: 999px; background: var(--agml-accent-soft); @@ -671,8 +676,7 @@ } .methodologyLink { - display: block; - margin: 0.6rem 0 0; + flex: 0 0 auto; border: none; background: none; padding: 0; @@ -680,6 +684,7 @@ font-family: var(--ifm-code-font-family); font-size: 0.72rem; color: var(--agml-muted); + white-space: nowrap; } .methodologyLink:hover, diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index 30280ff..f8d1693 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -446,6 +446,18 @@ function buildMetricCards(benchmark: BenchmarkData): MetricCardVM[] { return cards; } +// A plain ascending-bars glyph in currentColor — matches the rest of the header's monochrome +// icon language (×, ←) instead of a colorful emoji that clashes with it. +function BenchmarkIcon() { + return ( + + ); +} + function InfoTooltip({ text }: { text: string }) { return ( @@ -899,7 +911,6 @@ function BenchmarkView({ const scores = useMemo(() => computeScores(benchmark), [benchmark]); const scoreBoxes = useMemo(() => buildScoreBoxes(scores), [scores]); const reproRows = useMemo(() => buildReproRows(benchmark.reproducibility), [benchmark.reproducibility]); - const [showMethodology, setShowMethodology] = useState(false); const axisGroups = useMemo( () => METRIC_AXES.map((axis) => ({ axis, cards: cards.filter((card) => METRIC_AXIS_MAP[card.title] === axis) })).filter( @@ -941,10 +952,6 @@ function BenchmarkView({
)} - -
{axisGroups.map((group) => ( @@ -962,12 +969,6 @@ function BenchmarkView({

Visualization

- - setShowMethodology(false)} - />
); } @@ -1035,6 +1036,7 @@ export function DatasetMetadataModal({ const { data: benchmark, embeddings2d, embeddings3d } = useBenchmark(open ? (dataset?.name ?? null) : null); const [showBenchmarks, setShowBenchmarks] = useState(false); useEffect(() => setShowBenchmarks(false), [dataset?.name, open]); + const [showMethodology, setShowMethodology] = useState(false); const [metricTypeFilter, setMetricTypeFilter] = useState([]); const [tunedFilter, setTunedFilter] = useState([]); @@ -1170,7 +1172,18 @@ export function DatasetMetadataModal({
{benchmark && ( + )} + {benchmark && showBenchmarks && ( + )}
+ + setShowMethodology(false)} + />
); } From 42a4047c4bea67f61aacdea29b480b8a27ca3560 Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 13 Aug 2026 14:02:35 -0700 Subject: [PATCH 17/27] Move methodology link left of Dataset Details, restore its back icon Reorders the header so the scoring-methodology trigger reads before the flip toggle, and gives 'Dataset Details' a small back-arrow icon to match the bar-chart icon on 'View Benchmarks' now that neither button uses plain text arrows. --- src/components/DatasetMetadataModal.tsx | 28 +++++++++++++++++++------ 1 file changed, 22 insertions(+), 6 deletions(-) diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index f8d1693..f582cd5 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -458,6 +458,20 @@ function BenchmarkIcon() { ); } +function BackIcon() { + return ( + + ); +} + function InfoTooltip({ text }: { text: string }) { return ( @@ -1170,10 +1184,17 @@ export function DatasetMetadataModal({
+ {benchmark && showBenchmarks && ( + + )} {benchmark && ( )} - {benchmark && showBenchmarks && ( - - )} From 632e5d046848c89dd28368e5e2060267ab74223b Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 13 Aug 2026 14:17:23 -0700 Subject: [PATCH 18/27] Updated Benchmarking details for Image classification with updated metrics and pipeline changes --- ...ACHENY_variety_classification_umap_2d.json | 189188 ------------- ...ACHENY_variety_classification_umap_3d.json | 216242 --------------- ...ety_classification_bangladesh_umap_2d.json | 132750 --------- ...ety_classification_bangladesh_umap_3d.json | 152002 ---------- ...rmelon_disease_classification_umap_2d.json | 8088 +- ...rmelon_disease_classification_umap_3d.json | 2 +- 6 files changed, 2 insertions(+), 698270 deletions(-) delete mode 100644 static/data/dataset-benchmarking/embeddings/ACHENY_variety_classification_umap_2d.json delete mode 100644 static/data/dataset-benchmarking/embeddings/ACHENY_variety_classification_umap_3d.json delete mode 100644 static/data/dataset-benchmarking/embeddings/rice_variety_classification_bangladesh_umap_2d.json delete mode 100644 static/data/dataset-benchmarking/embeddings/rice_variety_classification_bangladesh_umap_3d.json diff --git a/static/data/dataset-benchmarking/embeddings/ACHENY_variety_classification_umap_2d.json b/static/data/dataset-benchmarking/embeddings/ACHENY_variety_classification_umap_2d.json deleted file mode 100644 index 0cab599..0000000 --- a/static/data/dataset-benchmarking/embeddings/ACHENY_variety_classification_umap_2d.json +++ /dev/null @@ -1,189188 +0,0 @@ -[ - { - "x": 8.0738, - "y": 17.4759, - "label": "AnaHau", - "split": "train", - "index": 0 - }, - { - "x": 8.5013, - "y": 17.354, - "label": "AnaHau", - "split": "test", - "index": 1 - }, - { - "x": 7.9579, - "y": 17.6188, - "label": "AnaHau", - "split": "train", - "index": 2 - }, - { - "x": 8.4359, - "y": 17.2972, 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\ No newline at end of file From d55bbaff238d551db866372139e06552fe8040d2 Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 13 Aug 2026 14:19:59 -0700 Subject: [PATCH 19/27] Add Image Text To Text tab to the scoring methodology reference Matches the task type used for VLM datasets elsewhere in the catalog. Falls through to the existing placeholder path since no metric formulas are published for it yet. --- src/components/ScoringMethodologyModal.tsx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/components/ScoringMethodologyModal.tsx b/src/components/ScoringMethodologyModal.tsx index 67b80ed..ed60f33 100644 --- a/src/components/ScoringMethodologyModal.tsx +++ b/src/components/ScoringMethodologyModal.tsx @@ -27,7 +27,7 @@ type AxisDoc = { axisFormula: string; }; -const TASK_TYPES = ['Image Classification', 'Object Detection', 'Image Segmentation'] as const; +const TASK_TYPES = ['Image Classification', 'Object Detection', 'Image Segmentation', 'Image Text To Text'] as const; type TaskType = (typeof TASK_TYPES)[number]; const AXIS_WEIGHTS: { label: string; pct: string; key: AxisKey }[] = [ From ccb539b74c27a3d12d1a09a3e93415e8a6a20bd0 Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 13 Aug 2026 16:31:05 -0700 Subject: [PATCH 20/27] Widen methodology metric cards to 2 per row, warm up light-mode amber tones MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Metric grid now uses 4 explicit tracks with each card spanning 2 (2 cards per row) instead of auto-fit, giving cards enough width that paragraphs stop wrapping into a narrow ladder. Added a 900px breakpoint so cards drop to one per row before the general 720px mobile breakpoint, since two cards get too cramped on tablet widths first. Also shifted the light-mode caution and warning tokens toward yellow — caution was a muddy amber (hue 75) and warning was a flat brown hex, both of which read as brown rather than yellow in the score boxes and penalty card. --- .../ScoringMethodologyModal.module.css | 32 ++++++++++++++++++- src/css/custom.css | 6 ++-- 2 files changed, 34 insertions(+), 4 deletions(-) diff --git a/src/components/ScoringMethodologyModal.module.css b/src/components/ScoringMethodologyModal.module.css index f85177e..854d503 100644 --- a/src/components/ScoringMethodologyModal.module.css +++ b/src/components/ScoringMethodologyModal.module.css @@ -247,14 +247,18 @@ border-left: 4px solid var(--axis-accent); } +/* 4 tracks with every card spanning 2 (so 2 cards per row) instead of auto-fit — matches the + wide-card pattern already used for the bar-chart cards on the main benchmark view, and gives + each card enough width that its paragraphs stop wrapping into a narrow ladder. */ .metricGrid { display: grid; - grid-template-columns: repeat(auto-fit, minmax(280px, 1fr)); + grid-template-columns: repeat(4, 1fr); gap: 1rem; margin-top: 0.9rem; } .metricCard { + grid-column: span 2; padding: 1rem 1.1rem; border-radius: 10px; border: 1px solid var(--agml-border); @@ -269,6 +273,7 @@ } .metricCardPenalty { + grid-column: span 2; padding: 1rem 1.1rem; border-radius: 10px; border: 1px solid var(--agml-border); @@ -496,6 +501,22 @@ text-align: right; } +/* Between mobile and the panel's own max width, 2-cards-per-row (each half of a ~980px panel) + gets tight enough that the formula boxes start cramping — drop to one full-width card per row + a bit before the general mobile breakpoint below. This block must come before the 720px block + so that, once both conditions are true on a narrow phone, the more specific 720px rules below + (later in source order, equal selector specificity) are the ones that win. */ +@media (max-width: 900px) { + .metricGrid { + grid-template-columns: repeat(2, 1fr); + } + + .metricCard, + .metricCardPenalty { + grid-column: span 2; + } +} + @media (max-width: 720px) { .panel { padding: 1.25rem; @@ -513,4 +534,13 @@ .header { flex-direction: column; } + + .metricGrid { + grid-template-columns: 1fr; + } + + .metricCard, + .metricCardPenalty { + grid-column: span 1; + } } diff --git a/src/css/custom.css b/src/css/custom.css index e90b66b..0123aaa 100644 --- a/src/css/custom.css +++ b/src/css/custom.css @@ -36,9 +36,9 @@ var(--ifm-color-primary) 35%, var(--agml-border) ); - --agml-warning-soft: rgba(189, 139, 76, 0.12); - --agml-warning-text: #7a4e2a; - --agml-caution-text: oklch(0.58 0.15 75); + --agml-warning-soft: rgba(196, 148, 58, 0.14); + --agml-warning-text: oklch(0.5 0.14 70); + --agml-caution-text: oklch(0.65 0.18 97); /* Per-axis accent hues for the scoring methodology reference (Structural Quality reuses --ifm-color-primary and Annotation Reliability reuses --agml-caution-text — both already sit at a matching hue, so only blue/pink need their own tokens). */ From f5354d505e89a07da019530a3c630432d6da1b5c Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 13 Aug 2026 16:43:16 -0700 Subject: [PATCH 21/27] Make the light-mode poor tier red instead of amber Warning and caution were both landing in the amber/gold family after the last pass, which left the Hard/poor tier too close to the Ambiguous/fair one to tell apart at a glance. Warning is now a clear red so the three tiers read as an actual green/yellow/red scale, matching how a reader expects severity levels to differ in color, not just lightness. Dark mode untouched. --- src/css/custom.css | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/css/custom.css b/src/css/custom.css index 0123aaa..e856f1f 100644 --- a/src/css/custom.css +++ b/src/css/custom.css @@ -36,8 +36,8 @@ var(--ifm-color-primary) 35%, var(--agml-border) ); - --agml-warning-soft: rgba(196, 148, 58, 0.14); - --agml-warning-text: oklch(0.5 0.14 70); + --agml-warning-soft: rgba(199, 60, 41, 0.12); + --agml-warning-text: oklch(0.55 0.19 27); --agml-caution-text: oklch(0.65 0.18 97); /* Per-axis accent hues for the scoring methodology reference (Structural Quality reuses --ifm-color-primary and Annotation Reliability reuses --agml-caution-text — both already From df34c3f4579f34cf374f5afd8435d1cf1fc2d8ef Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 13 Aug 2026 16:46:56 -0700 Subject: [PATCH 22/27] Give Image Text To Text its own badge color in the dataset modal taskBadgeClass() here never got the VLM branch that the datasets listing page's own copy of this function already has, so image-text-to-text tasks fell through to the generic 'Other' gray badge instead of the dedicated purple --agml-badge-vlm-* tokens that already existed for this. Ported the same check and added the matching .badgeVlm rule, which was likewise only defined on the listing page's CSS module. --- src/components/DatasetMetadataModal.module.css | 5 +++++ src/components/DatasetMetadataModal.tsx | 1 + 2 files changed, 6 insertions(+) diff --git a/src/components/DatasetMetadataModal.module.css b/src/components/DatasetMetadataModal.module.css index b42c85a..2a12491 100644 --- a/src/components/DatasetMetadataModal.module.css +++ b/src/components/DatasetMetadataModal.module.css @@ -69,6 +69,11 @@ color: var(--agml-badge-segmentation-fg); } +.badgeVlm { + background: var(--agml-badge-vlm-bg); + color: var(--agml-badge-vlm-fg); +} + .badgeOther { background: var(--agml-badge-other-bg); color: var(--agml-badge-other-fg); diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index 7a4e176..d1ff244 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -72,6 +72,7 @@ function formatResultType(entry: { variant: 'zero-shot' | 'fine-tuned' | null; o function taskBadgeClass(task: string | null): string { if (!task) return styles.badgeOther; + if (task.includes('image-text') || task.includes('text-to-text')) return styles.badgeVlm; if (task.includes('classif')) return styles.badgeClassification; if (task.includes('detect')) return styles.badgeDetection; if (task.includes('segment')) return styles.badgeSegmentation; From 64f168144612dff6b7daa531c90dc624f812b8d8 Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 13 Aug 2026 16:53:12 -0700 Subject: [PATCH 23/27] Make dark-mode poor tier red, title-case the locations list Dark mode's --agml-warning-text was still the old tan/amber color from before the light-mode red fix, so Hard and Ambiguous were just as hard to tell apart there as they were in light mode. Same green/yellow/red fix, bumped for dark backgrounds. The Locations list under Location & Collection rendered site.location raw instead of routing it through toTitleCase like the Country field next to it does, so lowercase source values (e.g. 'multinational') stayed lowercase. Also added a 'usa' override while in labelOverrides.ts, since the general auto title-case would otherwise turn 'USA' into 'Usa' wherever country goes through the same formatDisplayLocation path. --- src/components/DatasetMetadataModal.tsx | 2 +- src/css/custom.css | 4 ++-- src/lib/labelOverrides.ts | 3 +++ 3 files changed, 6 insertions(+), 3 deletions(-) diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index d1ff244..567f4dd 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -1251,7 +1251,7 @@ export function DatasetMetadataModal({
    {siteRows.map((site, index) => (
  • - {site.location && {site.location}} + {site.location && {toTitleCase(site.location)}} {site.coordinate && {site.coordinate}}
    • ))} diff --git a/src/css/custom.css b/src/css/custom.css index e856f1f..85b2ea1 100644 --- a/src/css/custom.css +++ b/src/css/custom.css @@ -91,8 +91,8 @@ html[data-theme="dark"] { var(--ifm-color-primary) 30%, var(--agml-border) ); - --agml-warning-soft: rgba(189, 139, 76, 0.18); - --agml-warning-text: #d9b084; + --agml-warning-soft: rgba(224, 90, 70, 0.18); + --agml-warning-text: oklch(0.7 0.18 27); --agml-caution-text: oklch(0.78 0.14 80); --agml-axis-difficulty: oklch(0.72 0.15 250); --agml-axis-diversity: oklch(0.74 0.15 340); diff --git a/src/lib/labelOverrides.ts b/src/lib/labelOverrides.ts index b29596f..a057e34 100644 --- a/src/lib/labelOverrides.ts +++ b/src/lib/labelOverrides.ts @@ -52,6 +52,9 @@ export const LABEL_OVERRIDES: Record = { lidar: "LIDAR", uv: "UV", ir: "IR", + + /* country */ + usa: "USA", }; function autoTitleCase(value: string): string { From 15bcdc7aa8c6d20300f6153838a1b6e9fbf86132 Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 13 Aug 2026 17:12:24 -0700 Subject: [PATCH 24/27] Updated package-lock for dependency mismatch between package.json and package-lock.json --- package-lock.json | 98 ++++++++++++++++++++++++++--------------------- 1 file changed, 54 insertions(+), 44 deletions(-) diff --git a/package-lock.json b/package-lock.json index 65dfda9..2980af1 100644 --- a/package-lock.json +++ b/package-lock.json @@ -8026,9 +8026,9 @@ } }, "node_modules/body-parser": { - "version": "1.20.5", - "resolved": "https://registry.npmjs.org/body-parser/-/body-parser-1.20.5.tgz", - "integrity": "sha512-3grm+/2tUOvu2cjJkvsIxrv/wVpfXQW4PsQHYm7yk4vfpu7Ekl6nEsYBoJUL6qDwZUx8wUhQ8tR2qz+ad9c9OA==", + "version": "1.20.6", + "resolved": "https://registry.npmjs.org/body-parser/-/body-parser-1.20.6.tgz", + "integrity": "sha512-p5tAzS57i5MV9fZFDj9LeIiTZEufbSe2eDozP+ElheSUq1m74CRq1jI4mYNDdVs9vQztXFLuk/Gd6BWTdwRJ5g==", "license": "MIT", "dependencies": { "bytes": "~3.1.2", @@ -8119,9 +8119,9 @@ } }, "node_modules/brace-expansion": { - "version": "1.1.15", - "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.15.tgz", - "integrity": "sha512-EwOCDEex4quD37XhqM3omwtMoJjr//isUZz1JopUNWms+4Z2ViyM/k1YIRePpoVNnQhENnxtFjLaxNHrT7xIUg==", + "version": "1.1.18", + "resolved": "https://registry.npmjs.org/brace-expansion/-/brace-expansion-1.1.18.tgz", + "integrity": "sha512-Edep/X9fGqVNmzKBVsDYIOtD+z1tuezV70LBjdCst9Tqu76lsnvRiZ6oTic1n+/BIwX6QDGAO94PN4N2SADvtw==", "license": "MIT", "dependencies": { "balanced-match": "^1.0.0", @@ -11149,9 +11149,9 @@ "license": "MIT" }, "node_modules/fast-uri": { - "version": "3.1.2", - "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.2.tgz", - "integrity": "sha512-rVjf7ArG3LTk+FS6Yw81V1DLuZl1bRbNrev6Tmd/9RaroeeRRJhAt7jg/6YFxbvAQXUCavSoZhPPj6oOx+5KjQ==", + "version": "3.1.5", + "resolved": "https://registry.npmjs.org/fast-uri/-/fast-uri-3.1.5.tgz", + "integrity": "sha512-gHwA1O9LDIcKunMKhObS/HimwtehO1nPUECKAu5TpKgaO19fcWEl4bliWe1jWxVFvIXztJjjQ4L8XQ1EU9f7Jw==", "funding": [ { "type": "github", @@ -12164,9 +12164,9 @@ } }, "node_modules/gray-matter/node_modules/js-yaml": { - "version": "3.14.2", - "resolved": "https://registry.npmjs.org/js-yaml/-/js-yaml-3.14.2.tgz", - "integrity": "sha512-PMSmkqxr106Xa156c2M265Z+FTrPl+oxd/rgOQy2tijQeK5TxQ43psO1ZCwhVOSdnn+RzkzlRz/eY4BgJBYVpg==", + "version": "3.15.1", + "resolved": "https://registry.npmjs.org/js-yaml/-/js-yaml-3.15.1.tgz", + "integrity": "sha512-S99WuO3HlhO3XN41EtYUNl9zzXjoJx7QvmipxsJVxtCBT0YHEFy+iOJhjSvrmV12nYhWpZaM8lPHkJm0yUMbag==", "license": "MIT", "dependencies": { "argparse": "^1.0.7", @@ -12721,9 +12721,9 @@ } }, "node_modules/http-proxy-middleware": { - "version": "2.0.9", - "resolved": "https://registry.npmjs.org/http-proxy-middleware/-/http-proxy-middleware-2.0.9.tgz", - "integrity": "sha512-c1IyJYLYppU574+YI7R4QyX2ystMtVXZwIdzazUIPIJsHuWNd+mho2j+bKoHftndicGj9yh+xjd+l0yj7VeT1Q==", + "version": "2.0.10", + "resolved": "https://registry.npmjs.org/http-proxy-middleware/-/http-proxy-middleware-2.0.10.tgz", + "integrity": "sha512-RKzRWNPxUZqbuk3BC5mGVJbBnWgr+diEnjJexIOytFbBzDy88Fbh/YvBr3DsNrl1jYAfjWfpATEv0NO35FDuPQ==", "license": "MIT", "dependencies": { "@types/http-proxy": "^1.17.8", @@ -13388,9 +13388,9 @@ } }, "node_modules/joi": { - "version": "17.13.3", - "resolved": "https://registry.npmjs.org/joi/-/joi-17.13.3.tgz", - "integrity": "sha512-otDA4ldcIx+ZXsKHWmp0YizCweVRZG96J10b0FevjfuncLO1oX59THoAmHkNubYJ+9gWsYsp5k8v4ib6oDv1fA==", + "version": "17.13.4", + "resolved": "https://registry.npmjs.org/joi/-/joi-17.13.4.tgz", + "integrity": "sha512-1RuuER6kmt8K8I3nIWvPZKi5RQCb568ZPyY4Pwjlua+yo+63ZTmIwxLZH0heBmiKN4uxjvCiarDrjaeH84xicQ==", "license": "BSD-3-Clause", "dependencies": { "@hapi/hoek": "^9.3.0", @@ -13407,9 +13407,19 @@ "license": "MIT" }, "node_modules/js-yaml": { - "version": "4.1.1", - "resolved": "https://registry.npmjs.org/js-yaml/-/js-yaml-4.1.1.tgz", - "integrity": "sha512-qQKT4zQxXl8lLwBtHMWwaTcGfFOZviOJet3Oy/xmGk2gZH677CJM9EvtfdSkgWcATZhj/55JZ0rmy3myCT5lsA==", + "version": "4.3.1", + "resolved": "https://registry.npmjs.org/js-yaml/-/js-yaml-4.3.1.tgz", + "integrity": "sha512-CY6crGq313MX8GkwvB7tzgp99vjQxY1++5y10/BKN/GUfHqWaOGQMNZkBvqSzsZKWk/ijwHlWzzkLulsGHhjWQ==", + "funding": [ + { + "type": "github", + "url": "https://github.com/sponsors/puzrin" + }, + { + "type": "github", + "url": "https://github.com/sponsors/nodeca" + } + ], "license": "MIT", "dependencies": { "argparse": "^2.0.1" @@ -13535,9 +13545,9 @@ } }, "node_modules/launch-editor": { - "version": "2.14.0", - "resolved": "https://registry.npmjs.org/launch-editor/-/launch-editor-2.14.0.tgz", - "integrity": "sha512-Pj3ZOx9dD1BClS7YcSQx0An1PCF9wz4JpvbEmKvDxQtm0jxlkk5NhW8x0SBAKA/acHBKZaqdd5FFOWlXo500JA==", + "version": "2.14.1", + "resolved": "https://registry.npmjs.org/launch-editor/-/launch-editor-2.14.1.tgz", + "integrity": "sha512-QWBrQsMpH7gPr965dsKD/3cKWiNoTjpATQf++Xq63N6sKRGMwlVXz41O1IZTMfZQgBctD/K5Zt06+/I6pP6+HA==", "license": "MIT", "dependencies": { "picocolors": "^1.1.1", @@ -16379,9 +16389,9 @@ "peer": true }, "node_modules/nanoid": { - "version": "3.3.12", - "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.12.tgz", - "integrity": "sha512-ZB9RH/39qpq5Vu6Y+NmUaFhQR6pp+M2Xt76XBnEwDaGcVAqhlvxrl3B2bKS5D3NH3QR76v3aSrKaF/Kiy7lEtQ==", + "version": "3.3.18", + "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-3.3.18.tgz", + "integrity": "sha512-DTg4MJbGMWkfi6VZFdNt2/caMbQy4Ou+Op/hJQvGEWcnVfoA1QA+xzRKAzw9jD6+GVOOeYr/mIcuDSdug6F6+w==", "funding": [ { "type": "github", @@ -17290,9 +17300,9 @@ "peer": true }, "node_modules/postcss": { - "version": "8.5.15", - "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.15.tgz", - "integrity": "sha512-FfR8sjd4em2T6fb3I2MwAJU7HWVMr9zba+enmQeeWFfCbm+UOC/0X4DS8XtpUTMwWMGbjKYP7xjfNekzyGmB3A==", + "version": "8.5.26", + "resolved": "https://registry.npmjs.org/postcss/-/postcss-8.5.26.tgz", + "integrity": "sha512-u82N74LFzG8ca+dD8puPnplTXoGH4fTPpVGuIbt36G3qvNlkvfD0lEAZSxaly3KX8TS/L1A1gsCEmvKmBcVbkQ==", "funding": [ { "type": "opencollective", @@ -17309,7 +17319,7 @@ ], "license": "MIT", "dependencies": { - "nanoid": "^3.3.12", + "nanoid": "^3.3.17", "picocolors": "^1.1.1", "source-map-js": "^1.2.1" }, @@ -20435,9 +20445,9 @@ } }, "node_modules/shell-quote": { - "version": "1.8.4", - "resolved": "https://registry.npmjs.org/shell-quote/-/shell-quote-1.8.4.tgz", - "integrity": "sha512-VsC6n6vz1ihYYyZZwX7YZSF5l5x36ca17OC+a69h94YqB7X6XLwf+5MOgynYir2SLFUbl8gIYvBo8K8RoNQ6bQ==", + "version": "1.10.0", + "resolved": "https://registry.npmjs.org/shell-quote/-/shell-quote-1.10.0.tgz", + "integrity": "sha512-w1aiOKwKuRgtwAReIIj89puqg+I7GvX4IbLrvmhXbzQsj1+Zwi4VO3+fa6ZF91TWSjIxoEkKnMeHcLEODK5ZXA==", "license": "MIT", "engines": { "node": ">= 0.4" @@ -21049,9 +21059,9 @@ } }, "node_modules/svgo": { - "version": "3.3.3", - "resolved": "https://registry.npmjs.org/svgo/-/svgo-3.3.3.tgz", - "integrity": "sha512-+wn7I4p7YgJhHs38k2TNjy1vCfPIfLIJWR5MnCStsN8WuuTcBnRKcMHQLMM2ijxGZmDoZwNv8ipl5aTTen62ng==", + "version": "3.3.4", + "resolved": "https://registry.npmjs.org/svgo/-/svgo-3.3.4.tgz", + "integrity": "sha512-GsNRis4e8jxn2Y9ENz/8lbJ93CstG8svtMnuRaHbiF2LTJ5tK0/q3t/URPq9Zc7zVWBJnNnJMIp6bevK7bSmNg==", "license": "MIT", "dependencies": { "commander": "^7.2.0", @@ -22285,9 +22295,9 @@ } }, "node_modules/webpack-dev-server": { - "version": "5.2.4", - "resolved": "https://registry.npmjs.org/webpack-dev-server/-/webpack-dev-server-5.2.4.tgz", - "integrity": "sha512-GqDPGZN9bRqKBTkp4aWkobDDHMsrXKoGSdOH56smIri8qR0JG8gfL8/v/f/OZR3/OKXjG8uwJbFVhKm/FNU/UA==", + "version": "5.2.6", + "resolved": "https://registry.npmjs.org/webpack-dev-server/-/webpack-dev-server-5.2.6.tgz", + "integrity": "sha512-HNLRmamRvVavZQ+avceZifmv8hmdUjg43t6MI4SqJDwFdW7RPQwH5vzGhDRZSX59SgfbeHhLnq3g+uooWo7pVw==", "license": "MIT", "dependencies": { "@types/bonjour": "^3.5.13", @@ -22308,7 +22318,7 @@ "graceful-fs": "^4.2.6", "http-proxy-middleware": "^2.0.9", "ipaddr.js": "^2.1.0", - "launch-editor": "^2.6.1", + "launch-editor": "^2.14.1", "open": "^10.0.3", "p-retry": "^6.2.0", "schema-utils": "^4.2.0", @@ -22452,9 +22462,9 @@ } }, "node_modules/websocket-driver": { - "version": "0.7.4", - "resolved": "https://registry.npmjs.org/websocket-driver/-/websocket-driver-0.7.4.tgz", - "integrity": "sha512-b17KeDIQVjvb0ssuSDF2cYXSg2iztliJ4B9WdsuB6J952qCPKmnVq4DyW5motImXHDC1cBT/1UezrJVsKw5zjg==", + "version": "0.7.5", + "resolved": "https://registry.npmjs.org/websocket-driver/-/websocket-driver-0.7.5.tgz", + "integrity": "sha512-ZL2+3c7kMBdIRCMz6l8jQMHyGVxj+UL+xVk74Ombiciboca8rHa15L86B19E5oh1pL9Ii/uj54gtsIrZGMo6zA==", "license": "Apache-2.0", "dependencies": { "http-parser-js": ">=0.5.1", From cae69485f06c74971276de21d66d33ca42c00ce0 Mon Sep 17 00:00:00 2001 From: Urmil Chandarana Date: Thu, 13 Aug 2026 17:23:57 -0700 Subject: [PATCH 25/27] Regenerate package-lock.json with npm 11 (Node 24) to fix npm ci The lockfile committed in 15bcdc7 was generated with npm 10 on Node 22, which silently drops optionalDependencies-of-optionalDependencies (e.g. the per-platform @rspack/binding-*, @swc/core-*, and lightningcss-* packages declared by @rspack/binding rather than @rspack/core directly) from the lockfile instead of recording them as unmet-platform optional entries. That made npm ci fail on CI with dozens of 'Missing from lock file' errors even though package.json and package-lock.json's direct dependencies matched. Regenerated from a clean node_modules using Node 24 / npm 11 (matching the engines field in package.json), which records the full multi-platform optional dependency set correctly. Verified with npm ci --dry-run. --- package-lock.json | 1554 +++++++++++++++++++++++++++++++-------------- 1 file changed, 1084 insertions(+), 470 deletions(-) diff --git a/package-lock.json b/package-lock.json index 2980af1..126c821 100644 --- a/package-lock.json +++ b/package-lock.json @@ -4160,6 +4160,17 @@ "node": ">=20.0" } }, + "node_modules/@emnapi/core": { + "version": "1.11.3", + "resolved": "https://registry.npmjs.org/@emnapi/core/-/core-1.11.3.tgz", + "integrity": "sha512-zLpS5asjEb7lq8jYLq37N6XKaE41DIexlY1rF/z4/tIl3wo13Sqm28fRyfIsKZD+NZ8mM5RoKkpW/rBcuoSZSg==", + "license": "MIT", + "optional": true, + "dependencies": { + "@emnapi/wasi-threads": "1.2.3", + "tslib": "^2.4.0" + } + }, "node_modules/@emnapi/runtime": { "version": "1.11.3", "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.11.3.tgz", @@ -4170,6 +4181,16 @@ "tslib": "^2.4.0" } }, + "node_modules/@emnapi/wasi-threads": { + "version": "1.2.3", + "resolved": "https://registry.npmjs.org/@emnapi/wasi-threads/-/wasi-threads-1.2.3.tgz", + "integrity": "sha512-ELEBe8PsLvvJ6QMr0zLt8ffvOHW/dc1m3CEzNMg7aJUv3bMaoDtw2TXyDAwkYBuroxxuHEwhRTLJSe5sya547g==", + "license": "MIT", + "optional": true, + "dependencies": { + "tslib": "^2.4.0" + } + }, "node_modules/@esbuild/aix-ppc64": { "version": "0.28.2", "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.28.2.tgz", @@ -4747,6 +4768,9 @@ "cpu": [ "arm" ], + "libc": [ + "glibc" + ], "license": "LGPL-3.0-or-later", "optional": true, "os": [ @@ -4763,6 +4787,9 @@ "cpu": [ "arm64" ], + "libc": [ + "glibc" + ], "license": "LGPL-3.0-or-later", "optional": true, "os": [ @@ -4779,6 +4806,9 @@ "cpu": [ "ppc64" ], + "libc": [ + "glibc" + ], "license": "LGPL-3.0-or-later", "optional": true, "os": [ @@ -4795,6 +4825,9 @@ "cpu": [ "riscv64" ], + "libc": [ + "glibc" + ], "license": "LGPL-3.0-or-later", "optional": true, "os": [ @@ -4811,6 +4844,9 @@ "cpu": [ "s390x" ], + "libc": [ + "glibc" + ], 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{ + "node": ">=12" + }, + "funding": { + "url": "https://github.com/sponsors/sindresorhus" + } + }, "node_modules/unique-string": { "version": "3.0.0", "resolved": "https://registry.npmjs.org/unique-string/-/unique-string-3.0.0.tgz", @@ -22027,6 +22640,7 @@ "version": "8.3.2", "resolved": "https://registry.npmjs.org/uuid/-/uuid-8.3.2.tgz", "integrity": "sha512-+NYs2QeMWy+GWFOEm9xnn6HCDp0l7QBD7ml8zLUmJ+93Q5NF0NocErnwkTkXVFNiX3/fpC6afS8Dhb/gz7R7eg==", + "deprecated": "uuid@10 and below is no longer supported. For ESM codebases, update to uuid@latest. For CommonJS codebases, use uuid@11 (but be aware this version will likely be deprecated in 2028).", "license": "MIT", "bin": { "uuid": "dist/bin/uuid" From 3a52a93dcaded378d439914df54f50d04aa1227f Mon Sep 17 00:00:00 2001 From: Jared Smith Date: Thu, 13 Aug 2026 18:02:02 -0700 Subject: [PATCH 26/27] minor bug fixes --- .../DatasetMetadataModal.module.css | 19 +++++- src/components/DatasetMetadataModal.tsx | 64 +++++++++++++++---- src/components/ScoringMethodologyModal.tsx | 23 ++++--- src/lib/datasets.ts | 20 +++++- src/pages/datasets/index.tsx | 6 +- 5 files changed, 104 insertions(+), 28 deletions(-) diff --git a/src/components/DatasetMetadataModal.module.css b/src/components/DatasetMetadataModal.module.css index 2a12491..16dfeeb 100644 --- a/src/components/DatasetMetadataModal.module.css +++ b/src/components/DatasetMetadataModal.module.css @@ -1082,18 +1082,33 @@ text-transform: none; letter-spacing: normal; white-space: normal; + /* `display: none` rather than `visibility: hidden`: a visibility-hidden box is still laid + out, and an absolutely positioned one still contributes to the modal's scrollable overflow + region. With a 240px bubble anchored to every stat-tile icon, the tiles near the modal's + right edge pushed that region ~200px wide and gave the benchmark face a permanent + horizontal scrollbar while nothing was even visible. */ + display: none; opacity: 0; - visibility: hidden; pointer-events: none; + /* allow-discrete + @starting-style keep the fade across the display change on browsers that + support them; elsewhere the bubble simply appears instantly. */ transition: opacity 0.12s ease; + transition-behavior: allow-discrete; box-shadow: var(--agml-shadow-strong); z-index: 20; } .infoTooltip:hover .infoTooltipBubble, .infoTooltip:focus-visible .infoTooltipBubble { + display: block; opacity: 1; - visibility: visible; +} + +@starting-style { + .infoTooltip:hover .infoTooltipBubble, + .infoTooltip:focus-visible .infoTooltipBubble { + opacity: 0; + } } .metricCardBadge { diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index 567f4dd..674d706 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -1,4 +1,4 @@ -import { Fragment, useEffect, useMemo, useState } from 'react'; +import { Fragment, useEffect, useMemo, useRef, useState } from 'react'; import { useColorMode } from '@docusaurus/theme-common'; import type { Dataset } from '../lib/datasets'; import { formatCoordinateList, formatDisplayLocation, formatLocationList, toTitleCase } from '../lib/datasets'; @@ -473,13 +473,50 @@ function BackIcon() { ); } +// Breathing room between a clamped tooltip and the modal's content edge. +const TOOLTIP_EDGE_GAP = 4; + function InfoTooltip({ text }: { text: string }) { + const bubbleRef = useRef(null); + + // The bubble is absolutely positioned against its own 14px icon, so `position: absolute` + // alone can't also keep it inside the modal — one containing block resolves both axes, and + // re-anchoring it to something wider would detach it vertically from the icon. Measure it + // once it's laid out and translate it back inside instead. + const clamp = () => { + const bubble = bubbleRef.current; + if (!bubble) return; + // Reset first: measuring an already-shifted bubble would compound the offset on + // every subsequent hover. + bubble.style.transform = ''; + const boundary = bubble.closest(`.${styles.modal}`); + if (!boundary) return; + // The modal's *border-box* right edge sits outside both its vertical scrollbar and its + // 2rem padding, so clamping to it still let the bubble overflow. Walk in to the content + // edge instead: clientLeft skips the border, clientWidth stops before the scrollbar, and + // paddingRight takes off the rest. + const bounds = boundary.getBoundingClientRect(); + const paddingRight = parseFloat(getComputedStyle(boundary).paddingRight) || 0; + const contentRight = bounds.left + boundary.clientLeft + boundary.clientWidth - paddingRight; + const overflow = bubble.getBoundingClientRect().right - (contentRight - TOOLTIP_EDGE_GAP); + if (overflow > 0) bubble.style.transform = `translateX(${-overflow}px)`; + }; + + // Deferred a frame so the :hover rule has flipped the bubble to `display: block` — a + // display:none element measures as a zero-size rect. The fade starts from opacity 0, so + // the one-frame delay isn't visible. + const scheduleClamp = () => { + requestAnimationFrame(clamp); + }; + return ( - + - {text} + + {text} + ); } @@ -1030,14 +1067,21 @@ export function DatasetMetadataModal({ open: boolean; onClose: () => void; }) { + // Declared above the Escape effect below, which has to read it — the methodology sheet + // layers on top of this modal and owns Escape while it's open. + const [showMethodology, setShowMethodology] = useState(false); + useEffect(() => { if (!open) return; const previousOverflow = document.body.style.overflow; document.body.style.overflow = 'hidden'; + // Both modals listen on `window`, so stopPropagation from the methodology sheet wouldn't + // suppress this one (same event target). Skip closing here instead, so one Escape dismisses + // only the topmost layer rather than collapsing the whole stack. const onKeyDown = (event: KeyboardEvent) => { - if (event.key === 'Escape') onClose(); + if (event.key === 'Escape' && !showMethodology) onClose(); }; window.addEventListener('keydown', onKeyDown); @@ -1045,13 +1089,12 @@ export function DatasetMetadataModal({ document.body.style.overflow = previousOverflow; window.removeEventListener('keydown', onKeyDown); }; - }, [open, onClose]); + }, [open, onClose, showMethodology]); const datasetPerformance = useDatasetPerformance(open ? (dataset?.name ?? null) : null); const { data: benchmark, embeddings2d, embeddings3d } = useBenchmark(open ? (dataset?.name ?? null) : null); const [showBenchmarks, setShowBenchmarks] = useState(false); useEffect(() => setShowBenchmarks(false), [dataset?.name, open]); - const [showMethodology, setShowMethodology] = useState(false); const [metricTypeFilter, setMetricTypeFilter] = useState([]); const [tunedFilter, setTunedFilter] = useState([]); @@ -1399,11 +1442,10 @@ export function DatasetMetadataModal({ View on Hugging Face )} - {isVlm && dataset.parent_dataset && ( - - View Original Dataset - - )} + {/* "View Original Dataset" is hidden for now: VLM records carry `parent_dataset` + as a bare Hugging Face repo slug (e.g. "AgroMind/AgroMind"), not a URL, so + linking it directly resolved as a relative site link and 404'd. Restore this + once the field is normalized to an absolute URL upstream. */}
)} diff --git a/src/components/ScoringMethodologyModal.tsx b/src/components/ScoringMethodologyModal.tsx index ed60f33..401e0c5 100644 --- a/src/components/ScoringMethodologyModal.tsx +++ b/src/components/ScoringMethodologyModal.tsx @@ -105,7 +105,7 @@ const IMAGE_CLASSIFICATION_AXES: Record

- If an entire axis can’t be computed for a dataset, its weight is split evenly across the axes that - are available, so the overall score always adds up to a full weighted average. + If an entire axis can’t be computed for a dataset, it is dropped and the remaining axes are + renormalized against their own weights, so the overall score is always a full weighted average of + whatever was available rather than being dragged down by a missing axis.

- If just one metric inside an axis is missing rather than the whole axis, that metric’s weight is - split across the other metrics in the same axis instead. + An axis needs every one of its metrics to be scored. If any single metric inside an axis is missing, + the whole axis is dropped and its weight is redistributed as above. The one exception is Content + Difficulty, which falls back to the separability score on its own when a run hasn’t completed + the phase 3 cartography and confusability metrics.

- 0.75 and up is good + 7.5 and up is good - 0.50 to 0.75 is fair + 5.0 to 7.5 is fair - below 0.50 needs attention + below 5.0 needs attention

- Formulas below compute on a 0 to 10 internal scale. The dataset card displays each score normalized to 0 - to 1 by dividing by 10. The thresholds above are in those app units. + Every formula below computes on a 0 to 10 scale, and that is the same scale the dataset card displays, + rounded to one decimal place. The thresholds above are in those units.

diff --git a/src/lib/datasets.ts b/src/lib/datasets.ts index 86342a0..1429ee5 100644 --- a/src/lib/datasets.ts +++ b/src/lib/datasets.ts @@ -430,7 +430,7 @@ export function filterDatasets( let out = datasets; if (opts.includeChildren === false) { - out = out.filter((d) => !d.parent_dataset); + out = out.filter((d) => !isChildDataset(d)); } if (opts.q?.trim()) { @@ -684,10 +684,26 @@ export interface DatasetStats { taskTypeCount: number; } +// `parent_dataset` means two different things depending on the record. On vision datasets it +// marks a child/augmented variant of another AgML dataset, which shouldn't be counted or listed +// separately. On VLM records it's a pointer to the upstream source the dataset was built from +// (a Hugging Face repo slug), and those are standalone top-level datasets — so reading the field +// as "this is a child" there wrongly hid all six of them from the counts and listings. +export function isChildDataset(dataset: Dataset): boolean { + return Boolean(dataset.parent_dataset) && dataset.dataset_type !== "vlm"; +} + +// Key for counting *distinct* datasets: child variants collapse onto their parent so an +// augmented counterpart isn't counted twice, while VLM records key on their own name rather +// than the upstream repo slug (which names a Hugging Face repo, not an AgML dataset). +export function datasetGroupKey(dataset: Dataset): string { + return isChildDataset(dataset) ? dataset.parent_dataset! : dataset.name; +} + // Shared by the homepage stats row and the dataset search page's hero stats, so both // report the same real numbers instead of two slightly different ad-hoc counts. export function computeDatasetStats(datasets: Dataset[]): DatasetStats { - const topLevel = datasets.filter((dataset) => !dataset.parent_dataset); + const topLevel = datasets.filter((dataset) => !isChildDataset(dataset)); const imageCount = topLevel .filter((dataset) => !dataset.name.startsWith("iNatAg-mini")) .reduce((sum, dataset) => sum + (dataset.num_images ?? 0), 0); diff --git a/src/pages/datasets/index.tsx b/src/pages/datasets/index.tsx index c081c83..8034599 100644 --- a/src/pages/datasets/index.tsx +++ b/src/pages/datasets/index.tsx @@ -4,7 +4,7 @@ import { useHistory, useLocation } from '@docusaurus/router'; import { DatasetMetadataModal } from '../../components/DatasetMetadataModal'; import { MultiSelectDropdown } from '../../components/MultiSelectDropdown'; import styles from './index.module.css'; -import { computeDatasetStats, filterDatasets, formatPrimaryLocation, useDatasets } from '../../lib/datasets'; +import { computeDatasetStats, datasetGroupKey, filterDatasets, formatPrimaryLocation, useDatasets } from '../../lib/datasets'; import { toDisplayLabel } from '../../lib/labelOverrides'; import { useSemanticDatasetSearch } from '../../lib/semanticSearch'; @@ -343,7 +343,7 @@ export default function DatasetBrowserPage() { const fieldValue = dataset[filter.field] as string | string[] | null | undefined; return Array.isArray(fieldValue) ? fieldValue.includes(value) : fieldValue === value; }) - .map((dataset) => dataset.parent_dataset ?? dataset.name) + .map(datasetGroupKey) ).size, ]) ), @@ -442,7 +442,7 @@ export default function DatasetBrowserPage() { const displayed = useMemo(() => filtered.slice(0, showCount), [filtered, showCount]); const hasMore = filtered.length > showCount; const distinctDatasetCount = useMemo( - () => new Set(filtered.map((dataset) => dataset.parent_dataset ?? dataset.name)).size, + () => new Set(filtered.map(datasetGroupKey)).size, [filtered] ); const selectedDataset = useMemo( From 75c0d7b8172bb032439534697917dcd922b04e89 Mon Sep 17 00:00:00 2001 From: Jared Smith Date: Thu, 13 Aug 2026 19:06:21 -0700 Subject: [PATCH 27/27] a few other bug fixes --- src/components/DatasetMetadataModal.tsx | 5 ++--- src/lib/datasets.ts | 2 +- src/lib/scoring.ts | 9 +++++++-- 3 files changed, 10 insertions(+), 6 deletions(-) diff --git a/src/components/DatasetMetadataModal.tsx b/src/components/DatasetMetadataModal.tsx index 674d706..942d400 100644 --- a/src/components/DatasetMetadataModal.tsx +++ b/src/components/DatasetMetadataModal.tsx @@ -32,7 +32,6 @@ function formatValue(value: string | string[] | null | undefined) { if (value == null) return 'Unknown'; if (Array.isArray(value)) return value.length ? value.map(toTitleCase).join(', ') : 'Unknown'; return toTitleCase(value); - return value; } function formatArray(value: number[] | null) { @@ -921,7 +920,7 @@ function buildScoreBoxes(scores: AxisScores): ScoreBoxVM[] { { label: 'Annotation Reliability', value: scores.annotation }, ]; return entries - .filter((entry): entry is { label: string; value: number } => entry.value != null) + .filter((entry): entry is { label: string; value: number } => entry.value != null && !Number.isNaN(entry.value)) .map((entry) => ({ label: entry.label, value: entry.value.toFixed(1), tier: scoreTier(entry.value) })); } @@ -1233,7 +1232,7 @@ export function DatasetMetadataModal({ )} {dataset.agricultural_task && {formatValue(dataset.agricultural_task)}} {dataset.real_or_synthetic && {formatValue(dataset.real_or_synthetic)}} - {dataset.license && {formatValue(dataset.license)}} + {dataset.license && {dataset.license}}
diff --git a/src/lib/datasets.ts b/src/lib/datasets.ts index 1429ee5..fc5e244 100644 --- a/src/lib/datasets.ts +++ b/src/lib/datasets.ts @@ -405,7 +405,7 @@ function matchesFieldValue( if (Array.isArray(value)) { return mode === "containsAny" ? value.some((entry) => values.includes(entry)) - : value.some((entry) => values.includes(entry)); + : values.length === value.length && values.every((entry) => value.includes(entry)); } if (mode === "containsAny") { return values.some((entry) => diff --git a/src/lib/scoring.ts b/src/lib/scoring.ts index 7b0a1a0..a7e27eb 100644 --- a/src/lib/scoring.ts +++ b/src/lib/scoring.ts @@ -38,7 +38,8 @@ export function computeScores(benchmark: ImageClassificationBenchmark): AxisScor const redundancy = clamp(10 * (1 - combined / 0.2), 0, 10); const resolution = clamp(10 * (1 - m.resolution_consistency!.area_cv), 0, 10); const crossSplit = m.exact_duplicate!.cross_split_duplicates + m.near_duplicate!.cross_split_near_duplicates; - const penalty = clamp((crossSplit * 100) / m.exact_duplicate!.total_images, 0, 2); + const totalImages = m.exact_duplicate!.total_images; + const penalty = totalImages > 0 ? clamp((crossSplit * 100) / totalImages, 0, 2) : 0; axes.structural = clamp(0.4 * balance + 0.35 * redundancy + 0.25 * resolution - penalty, 0, 10); } @@ -64,7 +65,11 @@ export function computeScores(benchmark: ImageClassificationBenchmark): AxisScor // Axis 4 — Annotation Reliability if (p3 && has(m, 'label_noise')) { - axes.annotation = clamp(10 * Math.pow(1 - m.label_noise!.estimated_noise_rate / 0.1, 1.5), 0, 10); + // Base is clamped to [0, 1] before the fractional exponent: a negative base raised to a + // fractional power is undefined (NaN in JS), and the doc's intent is to floor the score at + // 0 once noise reaches 10%, not to produce NaN beyond that point. + const base = clamp(1 - m.label_noise!.estimated_noise_rate / 0.1, 0, 1); + axes.annotation = clamp(10 * Math.pow(base, 1.5), 0, 10); } const weights: Record = { structural: 0.3, difficulty: 0.25, diversity: 0.25, annotation: 0.2 };