| --- |
| language: |
| - en |
| task_categories: |
| - keypoint-detection |
| size_categories: |
| - 100K<n<1M |
| tags: |
| - coco |
| - ap-10k |
| - pose-estimation |
| - tsv |
| --- |
| |
| # COCO-Keypoints and AP-10K training TSVs |
|
|
| COCO 2017 person-keypoint training annotations with **no COCO image bytes**, |
| plus AP-10K split-1 training data with embedded original images. |
| This derived export contains training splits only. |
|
|
| | Directory | Image / TSV rows | Positive-view images | Instances | Image delivery | |
| | --- | ---: | ---: | ---: | --- | |
| | `coco` | 118,287 | 56,599 | 262,465 | Download on demand | |
| | `ap10k` | 7,023 | 7,023 | 9,122 | Embedded in TSV | |
|
|
| ## COCO: annotations and on-demand images |
|
|
| `coco/train.annotations.tsv` contains `image_id<TAB>annotation_JSON`. |
| Both `.lineidx` files contain annotation byte offsets, not row numbers. |
| This is the `coco-keypoints-external-images-v1` schema, distinct from the |
| embedded-image Rex envelope used by AP-10K. |
|
|
| Each row contains `image` (ID, filename, dimensions, license ID and original-byte |
| SHA-256) and `annotations` (instance/image/category IDs, original pixel-**xywh** |
| box, area, crowd flag, labeled-joint count and all 17 `[x,y,v]` keypoint triples). |
| Segmentation and unrelated image provenance fields are omitted. `metadata.json` |
| retains source information, license tables, joint names and skeletons. |
| Coordinates and visibility are unchanged: v=0 unlabeled, v=1 labeled/occluded, |
| v=2 visible. No image bytes or full segmentation annotation archive are included. |
|
|
| Install Pillow from `requirements.txt` and `huggingface_hub` for downloading. |
|
|
| ```python |
| from huggingface_hub import snapshot_download |
| import sys |
| root = snapshot_download( |
| repo_id="huuthientran/KeypointEstimation", repo_type="dataset", |
| allow_patterns=["coco/*", "coco_download.py", "requirements.txt"], |
| ) |
| sys.path.insert(0, root) |
| from coco_download import CocoKeypointsDataset |
| |
| train = CocoKeypointsDataset(f"{root}/coco", images="./coco-images", positive_only=True) |
| annotation = train.annotation(0) # no network access |
| sample = train[0] # fetches missing image; returns PIL RGB + annotation |
| ``` |
|
|
| Missing images are fetched from `https://s3.amazonaws.com/images.cocodataset.org/train2017/` and |
| stored as `coco-images/train2017/000000000000.jpg`. The loader checks SHA-256 and |
| raster dimensions, writes downloads atomically, and reuses valid existing files. |
| It preserves the stored orientation without EXIF transpose. Network errors or |
| invalid cached files raise an error rather than silently dropping a sample. |
| An existing COCO installation can be used by setting `images` to the directory |
| containing `train2017`. For bulk preparation: |
|
|
| ```bash |
| python coco_download.py --images ./coco-images --positive-only --workers 8 |
| ``` |
|
|
| Omit `--positive-only` to fetch all train images. `--limit 3` provides a smoke run. |
| The positive view selects images with a non-crowd, labeled, positive-size pose |
| box. All original instances within selected images remain; apply instance |
| eligibility in the training adapter. Empty-pose rows remain in the complete |
| annotation index but incur no image download unless requested. |
|
|
| ## AP-10K: embedded original images |
|
|
| ```python |
| # Download ap10k/* and loader.py from the same repository first. |
| from loader import RawTSVDataset |
| animals = RawTSVDataset(f"{root}/ap10k") |
| sample = animals.decode(animals[0]) |
| ``` |
|
|
| AP-10K uses `rex-tsv-keypoints-v1`: image rows contain |
| `image_byte_offset<TAB>base64_original_image_bytes`; annotation rows contain |
| `image_byte_offset<TAB>annotation_JSON`. Indexes are byte positions. Derived |
| `boxes[].bbox` is pixel-**xyxy**; complete original pixel-xywh annotations, |
| keypoints, visibility, crowd and area fields remain in `source_annotations`. |
| Original image metadata, categories, joint names, skeletons and complete original |
| annotations are included. Image bytes are unchanged. |
|
|
| These pose schemas require a model-specific pose adapter. They are not generic |
| box-grounding training targets. No clipping, normalization, quantization, |
| deduplication, or evaluation-overlap cleanup is applied. Humans and animals have |
| different joint definitions. AP-10K's `ready_for_training: false` indicates that |
| model formatting and selection policy are separate work. |
|
|
| ## Validation and provenance |
|
|
| Every COCO TSV row, index, selected field and positive-view entry was compared |
| with original training annotations. Image hashes come from the original-byte |
| inventory validated with full local raster decoding; COCO images are excluded |
| from this release. AP-10K passed all-image raster decoding, checksums, offsets, |
| source annotation mapping, instance coverage and positive-index validation. |
| Per-dataset manifests and validation reports are included. To check the release: |
|
|
| ```bash |
| sha256sum --check SHA256SUMS |
| ``` |
|
|
| `SHA256SUMS` covers all release files except itself. Source checksums remain in |
| manifests. AP-10K's original local build paths are provenance, not loader |
| requirements. Pin a Hub revision when downloading for reproducibility. |
|
|
| Original datasets: [COCO](https://cocodataset.org/) and |
| [AP-10K](https://github.com/AlexTheBad/AP-10K). This packaging adds no new license |
| to original images or annotations. Follow the original dataset terms and image |
| licenses. COCO license tables and per-image identifiers are retained. |
|
|
