Update README.md

README.md

@@ -37,7 +37,6 @@ Each row has exactly four columns:
 | `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
 
@@ -94,44 +93,6 @@ The RTMPose-M Hand5 model is trained on a mixture of hand datasets including COC
 
 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