datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
glami-1m-t2i-mteb
GLAMI-1M text-to-image retrieval
This MTEB-formatted derivative uses the complete 116,004-row official GLAMI-1M test split. Product names and descriptions are text queries and product images are the corpus. Repeated image IDs and exact repeated texts are deduplicated within each language, and qrels retain every observed text-image association.
The unchanged source archives are already hosted by the original authors in glami/glami-1m. GLAMI-1M-dataset--test-only.zip is pinned at… See the full description on the dataset page: https://huggingface.co/datasets/artist/glami-1m-t2i-mteb.GLAMI-Entity-Matching-Dataset
GLAMI Duplication Detection
Product-duplicate detection over GLAMI e-commerce listings: ~1.3M product
images plus multilingual titles, descriptions and attributes, with labelled
groups of items that do or do not refer to the same physical product.
Released under the Apache License 2.0 — see LICENSE.
TODO: describe how the labels were produced.
Structure
Config
Files
Contents
images
images/shard-*.parquet
itemId → image bytes, one row per product image… See the full description on the dataset page: https://huggingface.co/datasets/zidcenek/GLAMI-Entity-Matching-Dataset.glami-1m
GLAMI-1M contains 1.1 million fashion items, 968 thousand unique images and 1 million unique texts. It contains 13 languages, mostly European. And 191 fine-grained categories, for example we have 15 shoe types. It contains high quality annotations from professional curators and it also presents a difficult production industry problem.
Each sample contains an image, country code, name in corresponding language, description, target category and source of the label which can be of multiple types… See the full description on the dataset page: https://huggingface.co/datasets/glami/glami-1m.GLAMI-1M
This is fork of original dataset converted to dataset format.
GLAMI-1M contains 1.1 million fashion items, 968 thousand unique images and 1 million unique texts. It contains 13 languages, mostly European. And 191 fine-grained categories, for example we have 15 shoe types. It contains high quality annotations from professional curators and it also presents a difficult production industry problem.
Each sample contains an image, country code, name in corresponding language… See the full description on the dataset page: https://huggingface.co/datasets/pySilver/GLAMI-1M.glami-1m-mteb
GLAMI-1M MTEB multimodal classification
This is an MTEB-ready derivative of the official
glami/glami-1m
release for multilingual image+text fashion classification. The source is
pinned at revision befda45d8d4e8b8082bb8a1912d1f9eb9483991c and remains
licensed under Apache-2.0.
Each example contains the official product image, name and description
joined as text, and the official category ID as label. The complete
116,004-row human-labeled test split is unchanged.
To keep… See the full description on the dataset page: https://huggingface.co/datasets/artist/glami-1m-mteb.glam-extraction-benchmark
GLAM extraction benchmark
Structured extraction from cultural-heritage documents. The first configuration is
nls-index-cards: 98 manuscript catalogue cards from the National Library of Scotland.
Source and credits
Derived from NationalLibraryOfScotland/index-cards-eval,
revision 2a81070549d8493c2c538744a9dbbc1dc72cb146 (CC0). Images and checked outputs are preserved.
NLS cataloguers reviewed the model-drafted labels: 66 accepted as drafted, 32 corrected.
Drafting… See the full description on the dataset page: https://huggingface.co/datasets/small-models-for-glam/glam-extraction-benchmark.bl-crop-tighten-v1
bl-crop-tighten-v1
Training data for crop tightening on the British Library Book Images collection: 7,565 ABBYY picture-block crops (train 6,050 / validation 757 / test 758) with instance boxes and segmentation masks. The splits are book-safe — no book appears in more than one split (4,484 books total).
The labels are weak labels, not human annotations: tiiuae/Falcon-Perception-0.6B ran open-vocabulary segmentation over 8,400 stratified crops (embellishments, plates, medium… See the full description on the dataset page: https://huggingface.co/datasets/small-models-for-glam/bl-crop-tighten-v1.GLAMI-1M-remapped
This is fork of original dataset converted to dataset format with adjusted category names.
GLAMI-1M contains 1.1 million fashion items, 968 thousand unique images and 1 million unique texts. It contains 13 languages, mostly European. And 191 fine-grained categories, for example we have 15 shoe types. It contains high quality annotations from professional curators and it also presents a difficult production industry problem.
Each sample contains an image, country code, name in… See the full description on the dataset page: https://huggingface.co/datasets/pySilver/GLAMI-1M-remapped.index-card-detection-v3
Dataset Card for Archival Index Card Detection — mixed collections
A training dataset for object detection of index cards in archival scans. Combines four publicly-released collections — NLS Advocates Library single-card pages, US Navy Nurse Corps multi-card biographical sheets, Boston Public Library catalog cards, and Duke Rubenstein manuscript catalog cards — into a single object-detection schema.
Dataset Details
Dataset Description
1,425 archival scans… See the full description on the dataset page: https://huggingface.co/datasets/small-models-for-glam/index-card-detection-v3.index-card-detection-v5
Dataset Card for Archival Index Card Detection — v5 (ensemble-relabelled navy)
Refined version of small-models-for-glam/index-card-detection-v3. All NLS / BPL / Rubenstein rows are passed through unchanged. The 25 navy-nurse-corps rows have their bounding boxes re-labelled via a v3+v4 model ensemble plus human review, replacing the SAM3-only bootstrap from v3.
Dataset Details
Dataset Description
Same 1,425-row mixed-collection composition as v3. The… See the full description on the dataset page: https://huggingface.co/datasets/small-models-for-glam/index-card-detection-v5.synthetic-aat-materials
Synthetic AAT Materials Dataset
Dataset Description
This dataset contains 1000 synthetic examples of cultural heritage object descriptions paired with their materials as they would appear in the Getty Art & Architecture Thesaurus (AAT). The data is formatted for training conversational AI models, particularly Qwen3, to identify and extract materials from cultural heritage object descriptions.
Dataset Structure
Each example contains:
messages: Conversation… See the full description on the dataset page: https://huggingface.co/datasets/small-models-for-glam/synthetic-aat-materials.synthetic-linkedart-production-destructionsynthetic-parsed-names-yaml
Dataset Card for Synthetic Parsed Names (YAML)
This dataset contains approximately 500,000 synthetic examples of complex, unstructured historical names paired with their structured YAML equivalents. It is designed to fine-tune small open-source large language models (LLMs) to accurately parse cultural heritage name strings into isolated components (first names, last names, middle names, dates, titles, etc.) for de-duplication and structured data ingestion.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/small-models-for-glam/synthetic-parsed-names-yaml.autotrain-zyermpvktmz6qr9uqy4xfu8xscu2zyermpvktmz6qr9uqy4xfu8xscu2synthetic-linkedart-physical-characteristicsindex-card-blank-content
Index-card blank / content / divider classifier — dataset
Cropped single archival index cards labelled blank, content, or divider, for
training a tiny CPU pre-filter that skips blank/divider cards before expensive VLM metadata
extraction in card-catalogue digitisation pipelines.
Two collections: Boston Public Library (BPL) FRC shelf-list cards and National Library
of Scotland (NLS) Advocates Library cards. Styles differ, so evaluate per collection.
How it was made… See the full description on the dataset page: https://huggingface.co/datasets/small-models-for-glam/index-card-blank-content.GLAMI-1M-test-onlyarabic_glamour_prompts
Dataset Card for "arabic_glamour_prompts"
More Information needed
aat-real-world
Real-World AAT Materials Dataset
This dataset contains 189,523 real-world examples of cultural heritage object material descriptions paired with their corresponding Art & Architecture Thesaurus (AAT) material classifications.
Dataset Description
The dataset is designed for training models to extract material information from cultural heritage object descriptions. Each example consists of:
Input: A real material description from cultural heritage collections
Output:… See the full description on the dataset page: https://huggingface.co/datasets/small-models-for-glam/aat-real-world.hardstyleGLAMI-1M-convo-smallglamour276Mix of curated websites (html, css, js) generated by various models (mostly Claude Opus 4.8, GPT Codex 5.3, Gemini Flash) on random prompts generated by Claude using Glamour.
glamour-opusschneewolflabs/glamour169 and schneewolflabs/glamour276 filtered for Claude Opus generations only.
glam-datasets
GLAM Datasets
Heterogeneous manipulation demonstrations for
GLAM: Imitation from Heterogeneous Demonstrations using Grounded Latent-Action World Models
(project page · code).
Each task combines a small target set — action-labelled demonstrations on our dual-arm Kinova
robot (Frank) in MuJoCo — with a larger, action-free auxiliary set collected by a floating UMI
gripper, also in MuJoCo.
Contents
Archive
Task
Episodes
Size
stack_two.tar
task 1 — stack two… See the full description on the dataset page: https://huggingface.co/datasets/VVVVVVVVIC/glam-datasets.synthetic-parsed-namesglamour169Mix of curated websites (html, css, js) generated by various models (mostly Claude Opus 4.8, GPT Codex 5.3, Gemini Flash) on random prompts generated by Claude using Glamour.
glami-1m
GLAMI-1M contains 1.1 million fashion items, 968 thousand unique images and 1 million unique texts. It contains 13 languages, mostly European. And 191 fine-grained categories, for example we have 15 shoe types. It contains high quality annotations from professional curators and it also presents a difficult production industry problem.
Each sample contains an image, country code, name in corresponding language, description, target category and source of the label which can be of multiple types… See the full description on the dataset page: https://huggingface.co/datasets/Imogenhb/glami-1m.autotrain-ali-imagesautotrain-zyermpvktmz6qr9uqy4xfu8xscu2glamira-raw-data