Add sanitized dataset card

README.md

@@ -1,28 +1,164 @@
 ---
-dataset_info:
-  features:
-  - name: bboxes_xyxy
-    list: float32
-    length: 4
-  - name: bbox_scores
-    dtype: float32
-  - name: keypoints_xy
-    list:
-      list: float32
-      length: 2
-    length: 21
-  - name: keypoint_scores
-    list: float32
-    length: 21
-  splits:
-  - name: train
-    num_bytes: 3012672
-    num_examples: 11076
-  download_size: 3202787
-  dataset_size: 3012672
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+license: mit
+task_categories:
+- image-to-text
+tags:
+- hand-pose-estimation
+- keypoints
+- bounding-boxes
+- rtmpose
+- mmpose
+- 11k-hands
+pretty_name: 11k-Hands BBox Keypoint
+size_categories:
+- 10K<n<100K
 ---
+
+# 11k-Hands BBox Keypoint
+
+This dataset contains estimated hand bounding boxes and 21-point hand keypoints for [`ryushinn/11k-Hands`](https://huggingface.co/datasets/ryushinn/11k-Hands). It is an annotations-only sidecar dataset: it does **not** duplicate the source images.
+
+Rows preserve the same split and row order as the source dataset. To pair an annotation row with its image, load the same split from `ryushinn/11k-Hands` and use the same row index.
+
+## Dataset structure
+
+The dataset has one split:
+
+| Split | Rows |
+| --- | ---: |
+| `train` | 11,076 |
+
+Each row has exactly four columns:
+
+| Column | Type / shape | Description |
+| --- | --- | --- |
+| `bboxes_xyxy` | `float32[4]` | Highest-confidence detected hand box as `[x1, y1, x2, y2]` in source-image pixel coordinates. |
+| `bbox_scores` | `float32` | Confidence score for the selected hand bounding box. |
+| `keypoints_xy` | `float32[21][2]` | Estimated 21-point hand keypoints in source-image pixel coordinates. |
+| `keypoint_scores` | `float32[21]` | Confidence score for each keypoint. |
+
+No source indices, labels, local file paths, or provenance columns are stored in the dataset rows.
+
+## Keypoint order
+
+The 21 keypoints follow the COCO-WholeBody hand convention used by MMPose:
+
+| Index | Name |
+| ---: | --- |
+| 0 | wrist |
+| 1 | thumb1 |
+| 2 | thumb2 |
+| 3 | thumb3 |
+| 4 | thumb4 |
+| 5 | forefinger1 |
+| 6 | forefinger2 |
+| 7 | forefinger3 |
+| 8 | forefinger4 |
+| 9 | middle_finger1 |
+| 10 | middle_finger2 |
+| 11 | middle_finger3 |
+| 12 | middle_finger4 |
+| 13 | ring_finger1 |
+| 14 | ring_finger2 |
+| 15 | ring_finger3 |
+| 16 | ring_finger4 |
+| 17 | pinky_finger1 |
+| 18 | pinky_finger2 |
+| 19 | pinky_finger3 |
+| 20 | pinky_finger4 |
+
+Skeleton edges used for visualization:
+
+```python
+[
+    (0, 1), (1, 2), (2, 3), (3, 4),
+    (0, 5), (5, 6), (6, 7), (7, 8),
+    (0, 9), (9, 10), (10, 11), (11, 12),
+    (0, 13), (13, 14), (14, 15), (15, 16),
+    (0, 17), (17, 18), (18, 19), (19, 20),
+]
+```
+
+## Model configuration
+
+Annotations were generated with OpenMMLab models:
+
+- Hand detector: **RTMDet-Nano hand detector**
+  - Config: `rtmdet_nano_320-8xb32_hand.py`
+  - Checkpoint: `rtmdet_nano_8xb32-300e_hand-267f9c8f.pth`
+- Hand pose estimator: **RTMPose-M Hand5**
+  - Config: `rtmpose-m_8xb256-210e_hand5-256x256.py`
+  - Checkpoint: `rtmpose-m_simcc-hand5_pt-aic-coco_210e-256x256-74fb594_20230320.pth`
+
+The RTMPose-M Hand5 model is trained on a mixture of hand datasets including COCO-WholeBody-Hand, OneHand10K, FreiHand2D, RHD2D, and Halpe hand annotations.
+
+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_11k_hands_keypoints_hf.py \
+  --source-repo ryushinn/11k-Hands \
+  --splits train \
+  --chunk-size 1000 \
+  --det-batch-size 16 \
+  --device cuda:0 \
+  --output-dir data/hf_datasets/11k_hands_rtmpose_hand21_keypoints \
+  --work-dir data/hf_datasets/11k_hands_rtmpose_hand21_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/11k_hands_rtmpose_hand21_keypoints")
+ds.push_to_hub("ryushinn/11k-Hands-BBox-Keypoint", max_shard_size="500MB")
+PY
+```
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+images = load_dataset("ryushinn/11k-Hands", split="train")
+ann = load_dataset("ryushinn/11k-Hands-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 images with the estimated hand bounding box and keypoint skeleton overlayed.
+
+| Row 0 | Row 1 |
+| --- | --- |
+| ![Preview row 0](previews/preview_000000.jpg) | ![Preview row 1](previews/preview_000001.jpg) |
+
+| Row 5538 | Row 11075 |
+| --- | --- |
+| ![Preview row 5538](previews/preview_005538.jpg) | ![Preview row 11075](previews/preview_011075.jpg) |
+
+## Notes
+
+These annotations are model-estimated pseudo-labels, not manual ground-truth annotations. They are intended for research workflows where reproducible hand bounding boxes and hand pose estimates are useful alongside the original `ryushinn/11k-Hands` images.