| --- |
| 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`](https://huggingface.co/datasets/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: |
|
|
| ```python |
| [ |
| (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 |
|
|
| ```python |
| 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. |
|
|
