| --- |
| 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 |
| ``` |
|
|