Hugging Face's logo

Datasets:
NVEagle
/
LocateAnything-Data

Tasks:
Object Detection
ArXiv:
Tags:
visual-grounding
pointing
document-layout-analysis
gui-grounding
megatron-energon
webdataset
Libraries:
WebDataset
Dataset card Files
xet
 Community
LocateAnything-Data / README.md
exiawsh's picture
exiawsh
Add final English and Chinese dataset documentation
fec481a verified
preview code
|
Raw
History Blame
14.5 kB
metadata 
pretty_name: LocateAnything-Data
task_categories:
  - object-detection
tags:
  - visual-grounding
  - pointing
  - megatron-energon
  - webdataset
  - multimodal
size_categories:
  - 10M<n<100M

LocateAnything-Data

中文说明 | Hugging Face repository

Official release repository: NVEagle/LocateAnything-Data. This collection does not declare a single umbrella license. Before using any component, consult and comply with the license and terms of its original upstream dataset.

LocateAnything-Data is a spatial vision-language training collection packaged for indexed JSONL access and Megatron-Energon. It contains object detection, visual grounding, and pointing records. It deliberately excludes VQA, benchmark, test, validation, chat, and internal build-provenance data.

Dataset inventory:

Item Count
Dataset folders 42
Annotation views 47
Training records 10,136,648
Canonical media pools 41
Referenced physical images 9,801,026 total; 7,402,667 hosted
Canonical TAR shards 926 in the source inventory; 583 hosted

OS-Atlas is part of this unified 42-dataset release. It is not published as a second copy or a separate media repository.

Media redistribution boundary

All 47 annotation views and the sanitized record-to-source-image mappings are in release scope. Images from the following seven upstream sources are not redistributed. Users must obtain those images from the official source and hydrate them locally.

Upstream source LocateAnything dataset ID Canonical pool ID Records Official access Expected source-relative path
CrowdHuman crowdhuman locany--crowdhuman--train--source_image--v000001 15,000 download train/Images/<name>.jpg
DeepFashion2 deepfashion2 locany--deepfashion2--train--source_image--v000001 191,961 instructions, request form train/image/000001.jpg
Flickr30K flickr30k locany--flickr30k--train--source_image--v000001 29,781 official page flickr30k-images/<flickr_id>.jpg
ImageNet partimagenet locany--imagenet--train--source_image--v000001 100,000 download/request access train/<wnid>/<wnid>_*.JPEG
Objects365 object365 locany--object365--train--source_image--v000001 1,742,289 download and terms images/train/patch*/objects365_v1_*.jpg
SKU-110K sku110k locany--sku110k--train--source_image--v000001 8,219 author repository images/train_*.jpg
Unsplash unsplash locany--unsplash--train--source_image--v000001 329,721 dataset access, dataset terms raw_unsplash_images/<photo_id>.jpg

ImageNet is a media source, not a LocateAnything dataset folder. Its images are used by the partimagenet annotation view. Objects365 is the official upstream name; object365 is the stable LocateAnything dataset ID.

These seven sources account for 2,416,971 records, 2,398,359 physical images, and 343 internal TAR shards. None of those TAR payloads are part of the public repository. The remaining hosted subset contains 35 dataset folders, 7,719,677 records, 34 media pools, 7,402,667 physical images, and 583 TAR shards. Every hosted TAR has exactly one repository path.

This repository is not a mirror of the seven upstream datasets and grants no rights to their media. Obtain the media independently, accept the terms in effect at download time, and preserve filenames and directory layout. The hydration tooling does not bypass registration, request forms, passwords, or access controls.

Additional source-specific notes:

  • CrowdHuman requires the training image archives referenced by the mapping. Its official page states that the images must not be redistributed.
  • DeepFashion2 requires a per-user access request and a password for the archives. The released mapping references train/image.
  • Flickr30K images remain subject to Flickr's terms. The official project also offers a publicly distributable links-and-captions version.
  • The ImageNet view uses a 100,000-image subset of the ILSVRC-style training layout. Do not flatten synset directories.
  • Objects365 users should preserve the V1/V2 package filenames and use the official annotation file_name values rather than renaming images.
  • SKU-110K users should preserve the extracted image and CSV layout.
  • The Unsplash Lite release is not necessarily sufficient for this 329,721 image mapping. Obtain the matching authorized release and require SHA-256 verification; do not substitute similarly named images.

Repository layout

The repository keeps one canonical path for every payload:

LocateAnything-Data/
├── .gitattributes
├── README.md
├── README_CN.md
├── release-policy.json
├── metadataset.yaml                 # immediately usable hosted subset
├── metadataset-full.yaml            # all 42 datasets after local hydration
├── manifests/
│   ├── release.json
│   ├── files.jsonl
│   ├── datasets.jsonl
│   ├── views.jsonl
│   ├── media-pools.jsonl
│   └── restricted-media.json
├── datasets/
│   └── <dataset_id>/
│       ├── metadataset.yaml
│       └── views/
│           └── <view_id>/
│               ├── metadataset.yaml
│               ├── records.jsonl
│               └── records.jsonl.idx
├── media/
│   └── <pool_id>/
│       ├── availability.json
│       ├── .nv-meta/                # present only for hosted media
│       └── shards/                  # present only for hosted media
│           ├── image-NNNNNNNN.tar
│           └── image-NNNNNNNN.tar.idx
├── mappings/
│   ├── media/<pool_id>/part-NNNNN.parquet
│   └── views/<dataset_id>/<view_id>/part-NNNNN.parquet
├── schemas/
│   ├── media-source-map.schema.json
│   └── view-source-map.schema.json
├── examples/
│   ├── read_indexed_jsonl.py
│   └── read_energon.py
└── tools/
    ├── hydrate_restricted_media.py
    └── verify_release.py

There is intentionally no datasets/<dataset_id>/media/ tree. From datasets/<dataset_id>/views/<view_id>/metadataset.yaml, every auxiliary media path must be exactly ../../../../media/<pool_id>. The seven external pools contain only availability.json until a user hydrates them locally.

metadataset.yaml blends the 35 datasets whose media is hosted on Hugging Face. metadataset-full.yaml blends all 42 datasets and must fail fast until the seven external pools have been hydrated and validated.

Annotation format

Annotations remain human-previewable JSON Lines. One line is one independent training record. The following is an abridged schema example:

{
  "_source": {
    "schema": "eagle-energon.source-record/v1",
    "dataset_id": "crowdhuman",
    "view_id": "locate_anything_crowdhuman_crowdhuman",
    "sample_id": "crowdhuman:locate_anything_crowdhuman_crowdhuman:file:0009:row:000000000000",
    "source_annotation": "annotations/CrowdHuman/CrowdHuman.jsonl",
    "source_file_index": 9,
    "source_row": 0,
    "source_record_sha256": "<sha256>"
  },
  "image": {
    "kind": "image",
    "source": "media_00",
    "path": "m/000000000082.jpg"
  },
  "query": {
    "Person": [[1, 410, 168, 1000]]
  },
  "task_type": "detection_grounding"
}

query values are numeric points [x, y] or boxes [x1, y1, x2, y2]. The reader does not rescale or rewrite them. image.source selects the view's Energon auxiliary pool and image.path is the exact TAR member name. Never infer a source filename from the packed member name.

records.jsonl.idx is a little-endian uint64 array with N + 1 byte offsets for N records. Row i occupies [offset[i], offset[i + 1]), enabling O(1) indexed record access.

Preview records:

sed -n '1,3p' \
  datasets/object365/views/locate_anything_object365_object365/records.jsonl \
  | jq -c .

Read one indexed row without loading the file:

python examples/read_indexed_jsonl.py \
  datasets/object365/views/locate_anything_object365_object365/records.jsonl \
  100

Public source-image mapping

The public repository does not contain the internal SQLite mapping databases, which include absolute paths and filesystem evidence. Instead, it contains two normalized, Zstandard-compressed Parquet relations:

  1. mappings/media/<pool_id>/... has one row per upstream source-image alias. It maps the original dataset-relative filename/path and SHA-256 to a canonical member_name and content SHA-256.
  2. mappings/views/<dataset_id>/<view_id>/... has one row per record-media occurrence. It maps the record lineage and JSON pointer to the exact pool-local source_id and canonical member.

The second relation is required because byte-identical images can have multiple upstream filenames. Joining only on member_name would be ambiguous. The authoritative join is (pool_id, source_id). source_snapshot_id identifies the conversion/source-manifest snapshot; it must not be interpreted as an upstream dataset release name.

Example: recover the exact original source image for record row 12,911:

INSTALL parquet;
LOAD parquet;

SELECT
  v.row_id,
  v.source_sample_id,
  m.source_media_name,
  m.source_relative_path,
  m.source_sha256,
  m.member_name
FROM read_parquet(
  'mappings/views/crowdhuman/locate_anything_crowdhuman_crowdhuman/*.parquet'
) AS v
JOIN read_parquet(
  'mappings/media/locany--crowdhuman--train--source_image--v000001/*.parquet'
) AS m
USING (pool_id, source_id)
WHERE v.row_id = 12911;

Reverse lookup from an upstream path to all LocateAnything records:

SELECT v.dataset_id, v.view_id, v.row_id, v.source_sample_id
FROM read_parquet(
  'mappings/media/locany--crowdhuman--train--source_image--v000001/*.parquet'
) AS m
JOIN read_parquet(
  'mappings/views/crowdhuman/locate_anything_crowdhuman_crowdhuman/*.parquet'
) AS v
USING (pool_id, source_id)
WHERE m.source_relative_path =
  'train/Images/273271,1017c000ac1360b7.jpg';

The public mapping preserves original source filenames, not LMDB keys or packed member names. It never contains source_absolute_path, internal annotation paths, Lustre paths, usernames, inode/device metadata, symlink targets, access tokens, cookies, or signed URLs. See the schemas under schemas/.

Reading with Megatron-Energon

The storage contract targets megatron-energon==7.4.0. Use:

  • metadataset.yaml for the immediately readable hosted subset;
  • metadataset-full.yaml after all seven external pools are hydrated;
  • datasets/<dataset_id>/metadataset.yaml for one dataset; or
  • a view-level metadataset.yaml for debugging.

The format supports random indexed JSONL rows, indexed uncompressed TAR samples, multiple workers, rank-aware partitioning, and stream packing. For an exact without-replacement sample permutation, use one indexed row per Energon slice (max_samples_per_sequence=1), one epoch shuffle multiplier, and no second shuffle buffer. Packing should happen after sample randomization.

The repository provides a small task encoder and runnable loader in examples/read_energon.py:

python examples/read_energon.py metadataset.yaml --workers 8 --samples 3

A missing external pool is an error: training code must not silently skip records.

Hydrating the seven external media pools

The hydrator uses the public mapping as the sole file list:

python tools/hydrate_restricted_media.py \
  --repo-root /data/LocateAnything-Data \
  --dataset crowdhuman \
  --source-root /data/upstream/CrowdHuman

For every mapped source image it:

  1. resolve the exact source_relative_path, never basename-guess;
  2. verify byte length and SHA-256;
  3. preserve the stable member_name;
  4. content-deduplicate within the canonical pool;
  5. write approximately 4 GiB uncompressed TAR shards;
  6. build .tar.idx and official Energon .nv-meta;
  7. verify every annotation reference; and
  8. write a local receipt.

Hydrated media remains local and must never be pushed back to the Hugging Face repository.

Canonical storage and deduplication

  • NVEagle/LocateAnything-Data is the collection's single canonical Hugging Face dataset repository.
  • Every canonical TAR has exactly one media/<pool_id>/shards/ repository path.
  • Dataset-local media projections are not part of the repository.
  • OS-Atlas belongs to the unified catalog and has no separate copy.
  • manifests/files.jsonl lists every release object except itself, with one row per canonical repository path.

Hugging Face Xet may deduplicate chunks internally; this does not change the path-level storage contract above.

Licenses and upstream terms

LocateAnything-Data combines annotations and references derived from multiple upstream datasets, so this repository does not select or imply one unified license for all components. Each upstream dataset remains governed by its own license, terms of use, access conditions, and attribution requirements. Users must review the official upstream source linked in this README and comply with the applicable terms before downloading, hydrating, using, or redistributing any component. This repository does not grant additional rights to upstream media.

Citation

If you find this work valuable, please cite:

@article{wang2026locateanything,
  title={LocateAnything: Fast and high-quality vision-language grounding with parallel box decoding},
  author={Wang, Shihao and Liu, Shilong and Kuang, Yuanguo and Wei, Xinyu and Liu, Yangzhou and Li, Zhiqi and Man, Yunze and Chen, Guo and Tao, Andrew and Liu, Guilin and others},
  journal={arXiv preprint arXiv:2605.27365},
  year={2026}
}