Datasets:
File size: 2,144 Bytes
ffd25bb 9fd219d ffd25bb 9fd219d ffd25bb 9fd219d ffd25bb 9fd219d ffd25bb 9fd219d ffd25bb 9fd219d ffd25bb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 | ---
license: unknown
task_categories:
- other
language:
- vi
tags:
- sign-language
- vietnamese-sign-language
- pose-estimation
- dwpose
- skeleton
size_categories:
- 10K<n<100K
---
# Multi-VSL (front view) — DWPose skeletons
Whole-body 2D pose keypoints extracted with [DWPose](https://github.com/IDEA-Research/DWPose)
from the front-camera clips of the Multi-VSL Vietnamese Sign Language corpus.
- **28,406** clips, one `.npz` per clip
- Total size: ~4.6 GB
- Laid out as `data/<signer>/<clip>.npz` — 30 signer directories, 628–1,167 clips
each (HF rejects directories holding more than 10,000 files, so a flat tree is
not possible here)
## Contents of each `.npz`
| key | shape | dtype | description |
|---|---|---|---|
| `all_xy` | `(T, 128, 2)` | `float16` | keypoint pixel coordinates per frame |
| `all_score` | `(T, 128)` | `float16` | per-keypoint confidence |
| `detected` | `(T,)` | `int8` | 1 if a person was detected in the frame, else 0 |
| `frame_size` | `(2,)` | `int32` | source video frame size |
| `fps` | scalar | `float32` | source video frame rate |
`T` is the number of frames in the clip. The 128 keypoints follow the DWPose /
COCO-WholeBody layout: 17 body + 6 foot + 68 face + 42 hands (21 per hand).
## File naming
```
<session>___<view>_<device>_<signer>_<view>_<order>_<clip_index>.npz
```
e.g. `01_Co-Hien_100-200_1-2-3_0118___center_device10_signer01_center_ord1_100.npz`
## Usage
```python
import numpy as np
from huggingface_hub import hf_hub_download
path = hf_hub_download(
"Tri1/Multi-VSL-front-skeleton",
"data/signer01/01_Co-Hien_100-200_1-2-3_0118___center_device10_signer01_center_ord1_100.npz",
repo_type="dataset",
)
d = np.load(path)
xy, score = d["all_xy"], d["all_score"] # (T, 128, 2), (T, 128)
```
Download everything, or just one signer:
```python
from huggingface_hub import snapshot_download
snapshot_download("Tri1/Multi-VSL-front-skeleton", repo_type="dataset",
local_dir="skeleton")
snapshot_download("Tri1/Multi-VSL-front-skeleton", repo_type="dataset",
allow_patterns="data/signer01/*", local_dir="skeleton")
```
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