Add final English and Chinese dataset documentation

README.md

@@ -0,0 +1,356 @@
+---
+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](README_CN.md) | [Hugging Face repository](https://huggingface.co/datasets/NVEagle/LocateAnything-Data)
+
+> Official release repository:
+> [`NVEagle/LocateAnything-Data`](https://huggingface.co/datasets/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](https://www.crowdhuman.org/download.html) | `train/Images/<name>.jpg` |
+| DeepFashion2 | `deepfashion2` | `locany--deepfashion2--train--source_image--v000001` | 191,961 | [instructions](https://github.com/switchablenorms/DeepFashion2), [request form](https://docs.google.com/forms/d/e/1FAIpQLSeIoGaFfCQILrtIZPykkr8q_h9qQ5BoTYbjvf95aXbid0v2Bw/viewform) | `train/image/000001.jpg` |
+| Flickr30K | `flickr30k` | `locany--flickr30k--train--source_image--v000001` | 29,781 | [official page](https://shannon.cs.illinois.edu/DenotationGraph/data/index.html) | `flickr30k-images/<flickr_id>.jpg` |
+| ImageNet | `partimagenet` | `locany--imagenet--train--source_image--v000001` | 100,000 | [download/request access](https://www.image-net.org/download.php) | `train/<wnid>/<wnid>_*.JPEG` |
+| Objects365 | `object365` | `locany--object365--train--source_image--v000001` | 1,742,289 | [download and terms](https://www.objects365.org/download.html) | `images/train/patch*/objects365_v1_*.jpg` |
+| SKU-110K | `sku110k` | `locany--sku110k--train--source_image--v000001` | 8,219 | [author repository](https://github.com/eg4000/SKU110K_CVPR19) | `images/train_*.jpg` |
+| Unsplash | `unsplash` | `locany--unsplash--train--source_image--v000001` | 329,721 | [dataset access](https://unsplash.com/data), [dataset terms](https://github.com/unsplash/datasets/blob/master/TERMS.md) | `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:
+
+```text
+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:
+
+```json
+{
+  "_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:
+
+```bash
+sed -n '1,3p' \
+  datasets/object365/views/locate_anything_object365_object365/records.jsonl \
+  | jq -c .
+```
+
+Read one indexed row without loading the file:
+
+```bash
+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:
+
+```sql
+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:
+
+```sql
+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`:
+
+```bash
+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:
+
+```bash
+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:
+
+```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}
+}
+```

README_CN.md

@@ -0,0 +1,338 @@
+---
+pretty_name: LocateAnything-Data
+task_categories:
+  - object-detection
+tags:
+  - visual-grounding
+  - pointing
+  - megatron-energon
+  - webdataset
+  - multimodal
+size_categories:
+  - 10M<n<100M
+---
+
+# LocateAnything-Data
+
+[English](README.md) | [Hugging Face 仓库](https://huggingface.co/datasets/NVEagle/LocateAnything-Data)
+
+> 官方发布仓库：
+> [`NVEagle/LocateAnything-Data`](https://huggingface.co/datasets/NVEagle/LocateAnything-Data)。
+> 本集合不声明一个覆盖全部内容的统一许可证。使用任何组成部分前，请查阅并
+> 遵守对应原始上游数据集的许可证和使用条款。
+
+LocateAnything-Data 是面向空间视觉语言训练的数据集合，使用可索引 JSONL 和
+Megatron-Energon 组织。任务只包括目标检测、visual grounding 和 pointing；
+不包含 VQA、benchmark、test、validation、chat 或内部构建 provenance。
+
+数据集总盘点如下：
+
+| 项目 | 数量 |
+|---|---:|
+| Dataset folder | 42 |
+| 标注 view | 47 |
+| 训练 record | 10,136,648 |
+| Canonical media pool | 41 |
+| 被引用的物理图片 | 总计 9,801,026；其中托管 7,402,667 |
+| Canonical TAR shard | 源盘点 926；其中托管 583 |
+
+OS-Atlas 已经属于这个统一的 42-dataset 发布，不再单独建立一份媒体仓库或重复
+发布。
+
+## 图片再分发边界
+
+47 个标注 view 和脱敏后的 record-to-source-image mapping 都属于开源范围。
+以下 7 个上游来源的图片不能随仓库再分发，使用者必须从官方入口自行取得并在
+本地 hydrate：
+
+| 上游来源 | LocateAnything dataset ID | Canonical pool ID | Record | 官方入口 | 预期 source-relative path |
+|---|---|---|---:|---|---|
+| CrowdHuman | `crowdhuman` | `locany--crowdhuman--train--source_image--v000001` | 15,000 | [下载](https://www.crowdhuman.org/download.html) | `train/Images/<name>.jpg` |
+| DeepFashion2 | `deepfashion2` | `locany--deepfashion2--train--source_image--v000001` | 191,961 | [官方说明](https://github.com/switchablenorms/DeepFashion2)、[申请表](https://docs.google.com/forms/d/e/1FAIpQLSeIoGaFfCQILrtIZPykkr8q_h9qQ5BoTYbjvf95aXbid0v2Bw/viewform) | `train/image/000001.jpg` |
+| Flickr30K | `flickr30k` | `locany--flickr30k--train--source_image--v000001` | 29,781 | [官方页面](https://shannon.cs.illinois.edu/DenotationGraph/data/index.html) | `flickr30k-images/<flickr_id>.jpg` |
+| ImageNet | `partimagenet` | `locany--imagenet--train--source_image--v000001` | 100,000 | [下载/申请访问](https://www.image-net.org/download.php) | `train/<wnid>/<wnid>_*.JPEG` |
+| Objects365 | `object365` | `locany--object365--train--source_image--v000001` | 1,742,289 | [下载及条款](https://www.objects365.org/download.html) | `images/train/patch*/objects365_v1_*.jpg` |
+| SKU-110K | `sku110k` | `locany--sku110k--train--source_image--v000001` | 8,219 | [作者仓库](https://github.com/eg4000/SKU110K_CVPR19) | `images/train_*.jpg` |
+| Unsplash | `unsplash` | `locany--unsplash--train--source_image--v000001` | 329,721 | [数据入口](https://unsplash.com/data)、[Dataset Terms](https://github.com/unsplash/datasets/blob/master/TERMS.md) | `raw_unsplash_images/<photo_id>.jpg` |
+
+ImageNet 在这里是媒体来源，不是一个 LocateAnything dataset folder；使用它的标注
+数据集是 `partimagenet`。Objects365 是上游官方名称，`object365` 只是
+LocateAnything 中的稳定 ID。
+
+这 7 个来源覆盖 2,416,971 条 record、2,398,359 个物理图片和内部 343 个 TAR；
+这些 TAR payload 一个都不会进入公开仓库。可以直接托管的其余部分包含 35 个
+dataset folder、7,719,677 条 record、34 个媒体池、7,402,667 个物理图片和
+583 个 TAR。每个托管 TAR 在仓库里只有一个路径。
+
+本仓库不是上述 7 个上游数据集的镜像，也不授予其媒体权利。使用者必须自行从
+官方入口取得媒体、接受下载时有效的条款，并保持原始文件名和目录结构。
+Hydration 工具不会绕过注册、申请表、密码或访问控制。
+
+补充下载说明：
+
+- CrowdHuman 需要下载 mapping 所引用的训练图片分卷；官网明确规定图片不得
+  再分发。
+- DeepFashion2 需要逐用户申请并取得压缩包密码；公开 mapping 引用
+  `train/image`。
+- Flickr30K 图片继续受 Flickr 条款约束；官方项目另行提供可公开分发的图片链接
+  与 captions 版本。
+- ImageNet view 使用 ILSVRC 风格训练目录中的 100,000 张图片，不能把 synset
+  目录打平。
+- Objects365 应保留 V1/V2 包的文件名，并按官方标注中的 `file_name` 关联，
+  不能自行改名。
+- SKU-110K 应保持解压后的图片和 CSV 目录结构。
+- Unsplash Lite 不一定覆盖本 mapping 的 329,721 张图片。必须取得匹配的授权
+  release 并用 SHA-256 校验，不能用同名或相似图片替换。
+
+## 仓库组织
+
+仓库对每个 payload 只保留一个 canonical 路径：
+
+```text
+LocateAnything-Data/
+├── .gitattributes
+���── README.md
+├── README_CN.md
+├── release-policy.json
+├── metadataset.yaml                 # 可立即读取的托管子集
+├── metadataset-full.yaml            # 本地补齐 7 个媒体池后的全集
+├── 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/                # 只存在于托管媒体池
+│       └── shards/                  # 只存在于托管媒体池
+│           ├── 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
+```
+
+仓库中禁止出现 `datasets/<dataset_id>/media/`。从
+`datasets/<dataset_id>/views/<view_id>/metadataset.yaml` 出发，每个 auxiliary
+媒体路径必须精确写成 `../../../../media/<pool_id>`。7 个外部媒体池在使用者
+本地 hydrate 前只有 `availability.json`。
+
+`metadataset.yaml` 混合 35 个图片已由 Hugging Face 托管的数据集；
+`metadataset-full.yaml` 混合全部 42 个数据集，在 7 个外部池全部 hydrate 并
+通过校验前必须 fail fast。
+
+## 标注格式
+
+标注保持为方便预览的 JSON Lines，每行是一条独立训练 record。下面是删减后的
+schema 示例：
+
+```json
+{
+  "_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` 中的值是数值点 `[x, y]` 或数值框 `[x1, y1, x2, y2]`，读取器不会
+再次缩放或改写。`image.source` 选择 view 的 Energon auxiliary pool，
+`image.path` 是 TAR 中的精确 member name。禁止根据 packed member name 猜测
+原始文件名。
+
+`records.jsonl.idx` 是 little-endian uint64 数组。N 条 record 对应 N+1 个
+uint64 offset（每个 8 字节），第 i 行范围为
+`[offset[i], offset[i + 1])`，因此可以 O(1) 定位。
+
+预览：
+
+```bash
+sed -n '1,3p' \
+  datasets/object365/views/locate_anything_object365_object365/records.jsonl \
+  | jq -c .
+```
+
+不扫描整个文件、直接读取指定行：
+
+```bash
+python examples/read_indexed_jsonl.py \
+  datasets/object365/views/locate_anything_object365_object365/records.jsonl \
+  100
+```
+
+## 公开 source-image mapping
+
+公开仓库不包含内部 SQLite mapping，因为其中含有绝对路径和文件系统证据。
+公开版使用两张规范化、Zstandard 压缩的 Parquet 关系表：
+
+1. `mappings/media/<pool_id>/...`：每个上游 source-image alias 一行，把原始
+   dataset-relative 文件名/路径及 SHA-256 映射到 canonical `member_name` 和
+   content SHA-256。
+2. `mappings/views/<dataset_id>/<view_id>/...`：每个 record-media occurrence
+   一行，把 record lineage 和 JSON pointer 映射到精确的 pool-local
+   `source_id` 及 canonical member。
+
+第二张表不可省略，因为字节完全相同的图片可能有多个上游文件名。只根据
+`member_name` 反查会产生歧义；唯一权威 join key 是
+`(pool_id, source_id)`。
+`source_snapshot_id` 表示转换/source-manifest snapshot，不能把它解释为
+上游数据集的 release 名称。
+
+从第 12,911 行 record 找回精确上游图片：
+
+```sql
+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;
+```
+
+从原始路径反查所有 LocateAnything record：
+
+```sql
+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';
+```
+
+公开 mapping 保留原始 source 文件名，不使用 LMDB key 或 packed member 冒充
+原始名字。mapping 绝不包含 `source_absolute_path`、内部 annotation 路径、
+Lustre 路径、用户名、inode/device、软链接目标、token、cookie 或 signed URL。
+精确字段见 `schemas/`。
+
+## 使用 Megatron-Energon 读取
+
+格式绑定 `megatron-energon==7.4.0`：
+
+- 直接可读的托管子集使用 `metadataset.yaml`；
+- 7 个外部池全部 hydrate 后使用 `metadataset-full.yaml`；
+- 单数据集使用 `datasets/<dataset_id>/metadataset.yaml`；
+- 调试单 view 时使用对应 view 的 `metadataset.yaml`。
+
+该结构支持 JSONL 随机索引、未压缩 TAR 随机索引、多 worker、按 rank 切分和
+stream packing。完全无放回的 sample-level permutation 应使用
+`max_samples_per_sequence=1`、单 epoch shuffle multiplier，并关闭第二层
+shuffle buffer；packing 放在样本随机化之后执行。
+
+仓库在 `examples/read_energon.py` 中提供轻量 task encoder 和可运行的 loader：
+
+```bash
+python examples/read_energon.py metadataset.yaml --workers 8 --samples 3
+```
+
+外部媒体池缺失必须报错，训练代码不得静默跳过 record。
+
+## 本地补齐 7 个外部媒体池
+
+Hydrator 只以公开 mapping 作为文件清单：
+
+```bash
+python tools/hydrate_restricted_media.py \
+  --repo-root /data/LocateAnything-Data \
+  --dataset crowdhuman \
+  --source-root /data/upstream/CrowdHuman
+```
+
+它会对每个 source image：
+
+1. 精确解析 `source_relative_path`，禁止只按 basename 猜测；
+2. 校验字节数和 SHA-256；
+3. 保持稳定的 `member_name`；
+4. 在 canonical pool 内按内容去重；
+5. 写约 4 GiB 的未压缩 TAR；
+6. 生成 `.tar.idx` 和官方 Energon `.nv-meta`；
+7. 验证全部 annotation reference；
+8. 写本地 hydration receipt。
+
+Hydrate 生成的媒体只能保留在本地，绝不能回传 Hugging Face。
+
+## Canonical 存储与去重
+
+- `NVEagle/LocateAnything-Data` 是本集合唯一的 canonical Hugging Face
+  dataset repository。
+- 每个 canonical TAR 在仓库中只有一个
+  `media/<pool_id>/shards/` 路径。
+- 仓库不包含 dataset-local media projection。
+- OS-Atlas 属于统一 catalog，不存在单独副本。
+- `manifests/files.jsonl` 覆盖除其自身以外的全部发布对象，每个 canonical
+  仓库路径恰好对应一行。
+
+Hugging Face Xet 可以在内部进行 chunk-level dedup；这不改变上述路径级存储
+约束。
+
+## 许可与上游条款
+
+LocateAnything-Data 汇集了来自多个上游数据集的标注和引用，因此本仓库不选择、
+也不暗示一个适用于全部组成部分的统一许可证。每个上游数据集仍受其各自的
+许可证、使用条款、访问条件和署名要求约束。下载、hydrate、使用或再分发任何
+组成部分前，使用者必须查阅本 README 中链接的官方上游来源并遵守适用条款。
+本仓库不授予任何额外的上游媒体权利。
+
+## 引用
+
+如果这项工作对您有价值，请引用：
+
+```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}
+}
+```