--- pretty_name: LocateAnything-Data task_categories: - object-detection tags: - visual-grounding - pointing - document-layout-analysis - gui-grounding - megatron-energon - webdataset - multimodal --- # LocateAnything-Data [中文](README_CN.md) · [Paper](https://arxiv.org/abs/2605.27365) · [Model](https://huggingface.co/nvidia/LocateAnything-3B) · [Code](https://github.com/NVlabs/Eagle/tree/main/Embodied) ## Overview LocateAnything-Data is the public training-data release for **LocateAnything: Fast and High-Quality Vision-Language Grounding with Parallel Box Decoding**. LocateAnything formulates detection and visual grounding as a unified vision-language task. Given an image and a category, phrase, text string, or action-oriented instruction, the model predicts the corresponding bounding boxes or points. The data spans natural images, dense scenes, people, autonomous driving, embodied interaction, graphical user interfaces, scene text, documents, and tables. The paper's Parallel Box Decoding treats a box or point as a structured geometric unit instead of generating its coordinates independently. LocateAnything-Data provides the diverse spatial supervision used to train this unified formulation across visual domains. This repository provides: - detection, grounding, and pointing annotations in previewable JSONL; - image media packed as indexed WebDataset TAR shards; - Megatron-Energon metadata for distributed training; and - public mappings from every training record and packed image back to its original dataset-relative media name. ## Data coverage The following grouping describes the main visual domains in this release. Several datasets naturally support more than one domain or task. | Domain | Included datasets | |---|---| | General detection and grounding | [COCO](datasets/coco/views/locate_anything_coco_coco), [RefCOCO](datasets/coco/views/locate_anything_coco_refcoco), [RefCOCO+](datasets/coco/views/locate_anything_coco_refcoco_plus), [RefCOCOg](datasets/coco/views/locate_anything_coco_refcocog), [G-RefCOCO](datasets/coco/views/locate_anything_coco_grefcoco), [Flickr30K](datasets/flickr30k), [HumanRef-CoT](datasets/humanref_cot), [Objects365](datasets/object365), [Open Images V6](datasets/openimages_v6), [OWDOD](datasets/owdod), [PACO](datasets/paco), [PartImageNet](datasets/partimagenet), [Unsplash](datasets/unsplash), [V3Det](datasets/v3det) | | Dense object detection | [CrowdHuman](datasets/crowdhuman), [DeepFashion2](datasets/deepfashion2), [HumanParts](datasets/humanparts), [MOT17Det](datasets/mot17det), [MOT20Det](datasets/mot20det), [SKU-110K](datasets/sku110k) | | Autonomous driving and robotics | [BDD100K](datasets/bdd100k), [EgoObjects](datasets/egoobjects), [nuImages](datasets/nuimages), [PixMo Points](datasets/pixmo_points), [RoboAfford](datasets/roboafford) | | GUI | [GroundCUA](datasets/groundcua), [OS-Atlas](datasets/os_atlas), [ScaleCUA](datasets/scalecua) | | Text recognition | [ArT](datasets/art_ocr), [BLIP3-OCR](datasets/blip3_ocr), [IDL-OCR](datasets/idlocr), [HierText](datasets/ocr_hiertext), [ICDAR 2013](datasets/ocr_icdar2013), [ICDAR 2015](datasets/ocr_icdar2015), [LSVT](datasets/ocr_lsvt), [RCTW](datasets/ocr_rctw), [ReCTS](datasets/ocr_rects), [SROIE](datasets/ocr_sroie), [TextOCR](datasets/ocr_textocr), [WildReceipt](datasets/ocr_wildreceipt) | | Documents, layouts, and tables | [CDLA](datasets/cdla), [DocLayNet](datasets/doclaynet), [M6Doc](datasets/m6doc), [PubLayNet](datasets/publaynet), [TableBank](datasets/tablebank), [TabRecSet](datasets/tabrecset) | ### Spatial supervision All records use a shared `query` representation: | `task_type` | Query target | Typical use | |---|---|---| | `detection_grounding` | Bounding box `[x1, y1, x2, y2]` | Category detection, phrase grounding, GUI grounding, OCR, and layout localization | | `pointing` | Point `[x, y]` | Point-based localization | Coordinates use the LocateAnything normalized spatial grid and can be mapped to image pixels with `x * width / 1000` and `y * height / 1000`. ## Release format Each dataset has its own folder and may contain one or more annotation **views**. A view is a training annotation variant over a media source—for example, COCO detection and RefCOCO grounding are separate views while sharing the same underlying images. ```text LocateAnything-Data/ ├── metadataset.yaml ├── metadataset-full.yaml ├── datasets/ │ └── / │ ├── metadataset.yaml │ └── views// │ ├── metadataset.yaml │ ├── records.jsonl │ └── records.jsonl.idx ├── media/ │ └── / │ ├── .nv-meta/ │ └── shards/ │ ├── image-NNNNNNNN.tar │ └── image-NNNNNNNN.tar.idx ├── mappings/ │ ├── media//part-*.parquet │ └── views///part-*.parquet ├── examples/ └── tools/ ``` - `records.jsonl` keeps annotations easy to inspect and process. - `records.jsonl.idx` stores byte offsets for direct row access. - Uncompressed TAR shards preserve encoded image bytes and support indexed reads without extracting millions of files. - `.tar.idx` and `.nv-meta` are consumed by Megatron-Energon. - Parquet mappings preserve record lineage and original media names. ### Annotation record Each JSONL line is one independent training record: ```json { "_source": { "dataset_id": "coco", "view_id": "locate_anything_coco_refcoco", "sample_id": "coco:locate_anything_coco_refcoco:..." }, "image": { "kind": "image", "source": "media_00", "path": "m/000000012345.jpg" }, "query": { "the person holding an umbrella": [[124, 86, 612, 944]] }, "task_type": "detection_grounding" } ``` `image.source` selects the auxiliary media pool declared by the view-level `metadataset.yaml`. `image.path` is the exact member name inside the TAR. ## Download Install the Hugging Face CLI and download the repository: ```bash pip install -U huggingface_hub hf download NVEagle/LocateAnything-Data \ --repo-type dataset \ --local-dir LocateAnything-Data ``` ### Download selected datasets Download the subset helper first: ```bash hf download NVEagle/LocateAnything-Data \ tools/download_subset.py \ --repo-type dataset \ --local-dir LocateAnything-Data ``` Then select datasets by their folder IDs: ```bash # Show all available dataset IDs. python LocateAnything-Data/tools/download_subset.py --list # Inspect the dependency closure and download size without downloading payloads. python LocateAnything-Data/tools/download_subset.py \ --dataset coco \ --local-dir LocateAnything-Data \ --dry-run # Download one dataset. python LocateAnything-Data/tools/download_subset.py \ --dataset coco \ --local-dir LocateAnything-Data # Download several datasets into the same local repository. python LocateAnything-Data/tools/download_subset.py \ --dataset coco,paco \ --local-dir LocateAnything-Data ``` The helper reads the release manifests and automatically includes the selected datasets' JSONL records, view mappings, media mappings, Energon metadata, and all required media pools. Shared dependencies are resolved automatically. For example, selecting `paco` also downloads the COCO media pool referenced by its annotations. To download annotations and mappings without hosted TAR payloads: ```bash python LocateAnything-Data/tools/download_subset.py \ --dataset coco,paco \ --annotations-only \ --local-dir LocateAnything-Data ``` For datasets whose images must be obtained from upstream, the helper downloads the annotations, mappings, and media-availability metadata, and reports that external media is still required. Follow [`tools/hydrate_restricted_media.py`](tools/hydrate_restricted_media.py) after obtaining those images. Subset download operates at dataset level. An individual annotation view can be copied separately, but it may still depend on the dataset's complete shared media pool. After a full or subset download, enter the local repository: ```bash cd LocateAnything-Data ``` ## Inspect annotations JSONL records can be previewed with standard command-line tools: ```bash sed -n '1,3p' \ datasets/coco/views/locate_anything_coco_refcoco/records.jsonl \ | jq . ``` Read a row by index without scanning the JSONL file: ```bash python examples/read_indexed_jsonl.py \ datasets/coco/views/locate_anything_coco_refcoco/records.jsonl \ 100 ``` ## Visualize a sample Install the runtime dependencies: ```bash pip install "megatron-energon==7.4.0" pillow ``` The following example loads one sample through Energon and draws all boxes and points: ```python from io import BytesIO from pathlib import Path from PIL import Image, ImageDraw from megatron.energon import ( WorkerConfig, get_savable_loader, get_train_dataset, ) from examples.read_energon import LocateAnythingTaskEncoder dataset = get_train_dataset( Path("datasets/ocr_icdar2013/metadataset.yaml"), split_part="train", worker_config=WorkerConfig( rank=0, world_size=1, num_workers=0 ), batch_size=None, max_samples_per_sequence=1, shuffle_over_epochs_multiplier=1, shuffle_buffer_size=None, task_encoder=LocateAnythingTaskEncoder(), repeat=False, ) sample = next(iter(get_savable_loader(dataset))) image = Image.open(BytesIO(sample.image)).convert("RGB") draw = ImageDraw.Draw(image) def to_pixel(target): values = list(target) for i in range(0, len(values), 2): values[i] = round(values[i] * image.width / 1000) values[i + 1] = round(values[i + 1] * image.height / 1000) return values for label, targets in sample.record["query"].items(): for target in targets: xy = to_pixel(target) if len(xy) == 4: draw.rectangle(xy, outline="red", width=3) draw.text((xy[0], xy[1]), label, fill="red") elif len(xy) == 2: x, y = xy draw.ellipse((x - 5, y - 5, x + 5, y + 5), fill="red") draw.text((x + 6, y), label, fill="red") image.save("sample.png") ``` ## Read for training The repository targets `megatron-energon==7.4.0`. Choose the entry point that matches your training scope: | Entry point | Purpose | |---|---| | `metadataset.yaml` | All datasets whose media is available directly in this repository | | `metadataset-full.yaml` | The full mixture after locally adding the externally downloaded media | | `datasets//metadataset.yaml` | One dataset | | `datasets//views//metadataset.yaml` | One annotation view | Run the included reader: ```bash python examples/read_energon.py \ datasets/coco/metadataset.yaml \ --workers 8 \ --samples 16 ``` `examples/read_energon.py` contains a minimal task encoder. It resolves the record's media source, reads the encoded image directly from the indexed TAR, and returns the annotation dictionary with image bytes. ## Map a record back to its source image Two Parquet relations connect each training record to its source image: - `mappings/views/...` connects a JSONL row to a media `source_id`. - `mappings/media/...` connects that `source_id` to the original dataset-relative filename, file hash, and packed TAR member. The authoritative join key is `(pool_id, source_id)`. For example: ```sql 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 = 100; ``` ## Media downloaded from upstream sources Media from the following datasets must be obtained from the original source. This repository includes their annotations, JSONL indexes, source-image mappings, and metadataset configuration, but not their image files. | Dataset folder | Download images from | Expected path under `--source-root` | |---|---|---| | `crowdhuman` | [CrowdHuman](https://www.crowdhuman.org/download.html) | `train/Images/.jpg` | | `deepfashion2` | [DeepFashion2](https://github.com/switchablenorms/DeepFashion2) | `train/image/.jpg` | | `flickr30k` | [Flickr30K](https://shannon.cs.illinois.edu/DenotationGraph/data/index.html) | `flickr30k-images/.jpg` | | `partimagenet` | [ImageNet](https://www.image-net.org/download.php) | `train//_.JPEG` | | `object365` | [Objects365](https://www.objects365.org/download.html) | `images/train/patch*/objects365_v1_.jpg` | | `sku110k` | [SKU-110K](https://github.com/eg4000/SKU110K_CVPR19) | `images/train_.jpg` | | `unsplash` | **TODO — reproducible media retrieval is not yet available** | `raw_unsplash_images/.jpg` | **Unsplash TODO.** The annotations and source-image mappings are included, but the current Unsplash distribution does not provide a stable procedure for reconstructing the exact historical image bytes referenced by this release. Unsplash media hydration is therefore not currently supported. We will document a reproducible retrieval and verification procedure when one is available. `metadataset.yaml` reads the datasets whose images are already hosted in this repository. Use `metadataset-full.yaml` only after all referenced external media pools are available locally. Unsplash is excluded from the currently supported reproducible hydration workflow. ### Prepare an upstream media dataset 1. Download the images from the official source and accept its terms. 2. Keep the upstream filenames and arrange the files under a source root using the layout shown above. 3. Install `megatron-energon==7.4.0`. 4. Run the hydration tool with the matching dataset ID and source root. ```bash python tools/hydrate_restricted_media.py \ --repo-root /data/LocateAnything-Data \ --dataset crowdhuman \ --source-root /data/upstream/CrowdHuman ``` Run this once for each of the seven dataset folders, changing `--dataset` and `--source-root` each time. The accepted dataset IDs are: ```text crowdhuman deepfashion2 flickr30k partimagenet object365 sku110k unsplash ``` The tool uses `mappings/media//part-*.parquet` as the file inventory. It resolves every `source_relative_path` under `--source-root`, verifies the file, and builds the local TAR indexes and Energon metadata required by `metadataset-full.yaml`. The tool prepares local media only; upstream downloads must be completed separately. ## License Information LocateAnything-Data is a collection built from multiple upstream datasets. Each source dataset remains governed by its original license, terms of use, access conditions, and attribution requirements. Users must review and comply with the applicable terms of each source dataset before use. This repository does not grant additional rights to upstream media. ### Copyright concerns This collection is prepared for academic research. If you believe that any content in LocateAnything-Data raises a copyright or attribution concern, please contact the maintainers through the [Hugging Face Community tab](https://huggingface.co/datasets/NVEagle/LocateAnything-Data/discussions). We will review the request and address verified concerns, including removing affected content when appropriate. ## Acknowledgements We thank the creators and maintainers of all datasets listed in [Data coverage](#data-coverage). Their work makes research on unified detection, grounding, pointing, GUI understanding, OCR, document understanding, autonomous driving, and robotics possible. If an important source or attribution is missing, please let us know through the [Hugging Face Community tab](https://huggingface.co/datasets/NVEagle/LocateAnything-Data/discussions). ## Citation If you find this work valuable, please cite: ```bibtex @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} } ```