Add sanitized dataset card

README.md

@@ -1,33 +1,159 @@
 ---
-dataset_info:
-  features:
-  - name: bboxes_xyxy
-    list: float32
-    length: 4
-  - name: bbox_scores
-    dtype: float32
-  - name: keypoints_xy
-    list:
-      list: float32
-      length: 2
-    length: 17
-  - name: keypoint_scores
-    list: float32
-    length: 17
-  splits:
-  - name: train
-    num_bytes: 198762816
-    num_examples: 887334
-  - name: test
-    num_bytes: 14380576
-    num_examples: 64199
-  download_size: 226749465
-  dataset_size: 213143392
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: test
-    path: data/test-*
+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. |
+
+No source indices, labels, local file paths, or provenance columns are stored in the dataset rows.
+
+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.
+
+## Re-create the dataset
+
+Install OpenMMLab dependencies in an environment with PyTorch and CUDA support:
+
+```bash
+pip install "setuptools>=70,<81" openmim
+mim install mmengine
+mim install "mmcv>=2.0.1,<2.2.0"
+mim install "mmdet>=3.1.0"
+mim install "mmpose>=1.1.0"
+```
+
+Generate the annotations:
+
+```bash
+python scripts/create_taichi_hd_keypoints_hf.py \
+  --source-repo ryushinn/Taichi-HD \
+  --splits train,test \
+  --chunk-size 10000 \
+  --det-batch-size 16 \
+  --device cuda:0 \
+  --output-dir data/hf_datasets/taichi_hd_rtmpose_coco17_keypoints \
+  --work-dir data/hf_datasets/taichi_hd_rtmpose_coco17_keypoints_chunks
+```
+
+The script is chunked and resumable. If interrupted, rerun the same command and completed chunks will be reused.
+
+Upload the generated dataset without uploading any metadata JSON:
+
+```bash
+python - <<'PY'
+from datasets import load_from_disk
+
+ds = load_from_disk("data/hf_datasets/taichi_hd_rtmpose_coco17_keypoints")
+ds.push_to_hub("ryushinn/Taichi-HD-BBox-Keypoint", max_shard_size="500MB")
+PY
+```
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+images = load_dataset("ryushinn/Taichi-HD", split="train")
+ann = load_dataset("ryushinn/Taichi-HD-BBox-Keypoint", split="train")
+
+idx = 0
+image = images[idx]["image"]
+bbox = ann[idx]["bboxes_xyxy"]
+keypoints_xy = ann[idx]["keypoints_xy"]
+keypoint_scores = ann[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 |
+| --- | --- |
+| ![Train row 0](previews/train_000000.jpg) | ![Train row 443667](previews/train_443667.jpg) |
+
+| Test row 0 | Test row 32099 |
+| --- | --- |
+| ![Test row 0](previews/test_000000.jpg) | ![Test row 32099](previews/test_032099.jpg) |
+
+## 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.