---
license: cc-by-nc-sa-4.0
task_categories:
- video-classification
- image-classification
language:
- fr
tags:
- sign_language
- sign_language_processing
- sign_language_recognition
- isolated_sign_language_recognition
- computer_vision
- natural_language_processing
pretty_name: LSFB-ISOL (Isolated French Belgian Sign Language Dataset)
configs:
  - config_name: "500"
    data_files:
      - split: train
        path:
          - "shards/500/shard_000003.tar"
          - "shards/500/shard_000004.tar"
          - "shards/500/shard_000005.tar"
          - "shards/500/shard_000006.tar"
          - "shards/500/shard_000007.tar"
          - "shards/500/shard_000008.tar"
          - "shards/500/shard_000009.tar"
      - split: validation
        path:
          - "shards/500/shard_000001.tar"
          - "shards/500/shard_000002.tar"
      - split: test
        path: "shards/500/shard_000000.tar"
  - config_name: "750"
    data_files:
      - split: train
        path:
          - "shards/750/shard_000003.tar"
          - "shards/750/shard_000004.tar"
          - "shards/750/shard_000005.tar"
          - "shards/750/shard_000006.tar"
          - "shards/750/shard_000007.tar"
          - "shards/750/shard_000008.tar"
          - "shards/750/shard_000009.tar"
      - split: validation
        path:
          - "shards/750/shard_000001.tar"
          - "shards/750/shard_000002.tar"
      - split: test
        path: "shards/750/shard_000000.tar"
  - config_name: "2000"
    data_files:
      - split: train
        path:
          - "shards/2000/shard_000003.tar"
          - "shards/2000/shard_000004.tar"
          - "shards/2000/shard_000005.tar"
          - "shards/2000/shard_000006.tar"
          - "shards/2000/shard_000007.tar"
          - "shards/2000/shard_000008.tar"
          - "shards/2000/shard_000009.tar"
      - split: validation
        path:
          - "shards/2000/shard_000001.tar"
          - "shards/2000/shard_000002.tar"
      - split: test
        path: "shards/2000/shard_000000.tar"
  - config_name: all
    data_files:
      - split: train
        path:
          - "shards/all/shard_000003.tar"
          - "shards/all/shard_000004.tar"
          - "shards/all/shard_000005.tar"
          - "shards/all/shard_000006.tar"
          - "shards/all/shard_000007.tar"
          - "shards/all/shard_000008.tar"
          - "shards/all/shard_000009.tar"
      - split: validation
        path:
          - "shards/all/shard_000001.tar"
          - "shards/all/shard_000002.tar"
      - split: test
        path: "shards/all/shard_000000.tar"
---

# LSFB-ISOL: Isolated French Belgian Sign Language Dataset

LSFB-ISOL is the **isolated-sign** subset of the [LSFB corpus](https://www.corpus-lsfb.be/): one pre-segmented, single-sign clip per sample, extracted from the continuous ([lsfb-cont](https://huggingface.co/datasets/ppoitier/lsfb-cont)) recordings. It is built for **Isolated Sign Language Recognition (ISLR)**.

<video controls src="https://huggingface.co/datasets/ppoitier/lsfb-isol/resolve/main/example.mp4" width="360"></video>

## Dataset structure

The dataset is distributed as [WebDataset](https://github.com/webdataset/webdataset) `.tar` shards, split into four vocabulary-size variants that share the same underlying data:

| Variant | Vocabulary |
|---|---|
| `500`   | 500 most frequent signs |
| `750`   | 750 most frequent signs |
| `2000`  | 2000 most frequent signs |
| `all`   | Full vocabulary |

Each variant is split into `train` (shards 3-9), `validation` (shards 1-2) and `test` (shard 0).

Each sample is keyed by a clip id (e.g. `CLSFBI0301A_S008_B_12100_12437`) and contains:

- `pose.<body_part>.npy` — keypoint sequence for one body part (`upper_pose`, `left_hand`, `right_hand`, `lips`, `left_eye`, `right_eye`, `left_eyebrow`, `right_eyebrow`, `left_iris`, `right_iris`)
- `label.txt` / `label.idx` — the sign gloss (e.g. `bonjour`) and its class index
- `signer.txt` — anonymized signer id (e.g. `S008`)
- `language.txt` — always `lsfb`
- `boundaries.json` — `start_ms` / `end_ms` / `start_frame` / `end_frame` of the clip within its parent recording
- `linguistic_metadata.json` — `gloss`, `lemma`, `sign_type`
- `parent.txt` — id of the source continuous recording (see [lsfb-cont](https://huggingface.co/datasets/ppoitier/lsfb-cont))

Raw video clips are stored separately in `videos.tar`, indexed by `videos.tar.index.json` (byte offset/length per clip), so a single clip can be extracted without downloading the whole archive. `example.mp4` is a preview clip.

## Usage

This dataset is designed to be loaded with [sign-language-data-loading](https://github.com/ppoitier/sign-language-data-loading) (`sldl`, [PyPI](https://pypi.org/project/sign-language-data-loading/)), which reads the WebDataset shards directly and handles pose/video decoding, windowing and target encoding:

```bash
pip install sign-language-data-loading
```

```python
from sldl import SignLanguageDataset
from sldl.configs import LSFBIsolConfig

dataset = SignLanguageDataset.from_config(
    LSFBIsolConfig(
        root="path/to/lsfb-isol",  # local clone or download of this repo
        variant="500",
        split="training",
    )
)

sample = dataset[0]
print(sample["label"])                             # e.g. "bonjour"
print(sample["poses"]["upper_pose"].shape)
```

Pose/annotation transforms (e.g. converting sign boundaries to frame labels) are provided by [sign-language-tools](https://github.com/ppoitier/sign-language-tools) (`slt`, [PyPI](https://pypi.org/project/sign-language-tools/)), which `sldl`'s target encoders build on.

## License

This dataset is distributed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).

## Related projects

- [sign-language-data-loading](https://github.com/ppoitier/sign-language-data-loading) ([PyPI](https://pypi.org/project/sign-language-data-loading/)) — PyTorch-friendly data loading for sign language datasets stored as WebDataset shards; this dataset's intended loader.
- [sign-language-tools](https://github.com/ppoitier/sign-language-tools) ([PyPI](https://pypi.org/project/sign-language-tools/)) — pose and annotation transforms for sign language processing.
- [lsfb-cont](https://huggingface.co/datasets/ppoitier/lsfb-cont) — the continuous (untrimmed) counterpart of this dataset, useful for Continuous Sign Language Recognition (CSLR) and Sign Language Segmentation (SLS).