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tags:
- sign-language
- how2sign
- features
---
# How2Sign — Extracted Features
Pre-computed features from the [How2Sign](https://huggingface.co/datasets/aipieces/How2Sign) dataset.
## Layout
Each modality is split by `train` / `test` / `val` and packed into ~3 GB tar shards.
| Modality | Content | Approx size |
|-------------------------------------|--------------------------------------------------|-------------|
| `depth_rendered` | rendered depth-map JPGs per clip | ~39 GB |
| `poses_rendered` | rendered pose-skeleton JPGs per clip | ~43 GB |
| `poses` | raw pose `.npy` per clip | ~18 GB |
| `optical_flow` | optical flow `.npy` per clip | ~3 GB |
| `optical_flow_rendered` | rendered optical_flow JPGs per clip with stride 2| ~11 GB |
| `processed_english_translations` | translations csv per split | ~6 MB |
Inside each tar, paths are relative to the split, e.g.:
<clip_name>/00000.jpg # rendered modalities
<clip_name>.npy # npy modalities
## Notes on `.npy` files
Poses `.npy` is a 0-d object array wrapping a Python object (typically a dict). Load with:
```python
import numpy as np
data = np.load("clip.npy", allow_pickle=True).item()
```
## Downloading
Everything:
```python
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="Alexeus17071/How2Sign_with_features",
repo_type="dataset",
local_dir="./how2sign_features",
)
```
Just one modality/split:
```python
snapshot_download(
repo_id="Alexeus17071/How2Sign_with_features",
repo_type="dataset",
local_dir="./how2sign_features",
allow_patterns=["poses/train/*"],
)
```
Extract:
```bash
mkdir -p extracted/poses/train
for f in how2sign_features/poses/train/*.tar; do
tar -xf "$f" -C extracted/poses/train/
done
```
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