README.md

metadata

license: mit
task_categories:
  - image-to-text
tags:
  - human-pose-estimation
  - keypoints
  - bounding-boxes
  - rtmpose
  - mmpose
  - taichi-hd
pretty_name: Taichi-HD BBox Keypoint
size_categories:
  - 100K<n<1M

Taichi-HD BBox Keypoint

This dataset contains estimated person bounding boxes and COCO-17 body keypoints for ryushinn/Taichi-HD. It is an annotations-only sidecar dataset: it does not duplicate the source images.

Rows preserve the same split names and row order as the source dataset. To pair an annotation row with its image, load the same split from ryushinn/Taichi-HD and use the same row index.

Dataset structure

Split	Rows	Detected person rows
train	887,334	887,302
test	64,199	64,191

Each row has exactly four columns:

Column	Type / shape	Description
bboxes_xyxy	float32[4]	Highest-confidence detected person box as [x1, y1, x2, y2] in source-image pixel coordinates.
bbox_scores	float32	Confidence score for the selected person bounding box.
keypoints_xy	float32[17][2]	Estimated COCO-17 body keypoints in source-image pixel coordinates.
keypoint_scores	float32[17]	Confidence score for each keypoint.

For missing detections, bboxes_xyxy and keypoints_xy contain NaN coordinates, bbox_scores is 0.0, and keypoint_scores contains zeros.

Keypoint order

The 17 keypoints follow the COCO body convention:

Index	Name
0	nose
1	left_eye
2	right_eye
3	left_ear
4	right_ear
5	left_shoulder
6	right_shoulder
7	left_elbow
8	right_elbow
9	left_wrist
10	right_wrist
11	left_hip
12	right_hip
13	left_knee
14	right_knee
15	left_ankle
16	right_ankle

Skeleton edges used for visualization:

[
    (15, 13), (13, 11), (16, 14), (14, 12),
    (11, 12), (5, 11), (6, 12), (5, 6),
    (5, 7), (6, 8), (7, 9), (8, 10),
    (1, 2), (0, 1), (0, 2), (1, 3),
    (2, 4), (3, 5), (4, 6),
]

Model configuration

Annotations were generated with OpenMMLab models:

• Person detector: RTMDet-M COCO person detector
  • Config: rtmdet_m_8xb32-300e_coco.py
  • Checkpoint: rtmdet_m_8xb32-300e_coco_20220719_112220-229f527c.pth
• Body pose estimator: RTMPose-M COCO
  • Config: rtmpose-m_8xb256-420e_coco-256x192.py
  • Checkpoint: rtmpose-m_simcc-coco_pt-aic-coco_420e-256x192-d8dd5ca4_20230127.pth

Reference: Jiang et al., RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose, arXiv:2303.07399.

Usage

from datasets import concatenate_datasets, load_dataset

images = load_dataset("ryushinn/Taichi-HD", split="train")
ann = load_dataset("ryushinn/Taichi-HD-BBox-Keypoint", split="train")

ds = concatenate_datasets([images, ann], axis=1)
# You may want to filter out low-confidence detections
# ds = ds.filter(lambda row: [v > 0.05 for v in row["bbox_scores"]], batched=True)
idx = 0
image = ds[idx]["image"]
bbox = ds[idx]["bboxes_xyxy"]
keypoints_xy = ds[idx]["keypoints_xy"]
keypoint_scores = ds[idx]["keypoint_scores"]

Visualization previews

The following preview images show source frames with the estimated person bounding box and COCO-17 skeleton overlayed.

Train row 0	Train row 443667

Test row 0	Test row 32099

Notes

These annotations are model-estimated pseudo-labels, not manual ground-truth annotations. They are intended for research workflows where reproducible person bounding boxes and COCO-17 body pose estimates are useful alongside the original ryushinn/Taichi-HD videos/frames.