huuthientran/KeypointEstimation

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.

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:

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

# 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:

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 and 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.