---
dataset_info:
  features:
  - name: landmarks
    dtype:
      array2_d:
        shape:
        - 64
        - 225
        dtype: float32
  - name: label
    dtype: string
  splits:
  - name: train
    num_bytes: 212201956.0
    num_examples: 3667
  - name: test
    num_bytes: 128582696.0
    num_examples: 2222
  download_size: 265851675
  dataset_size: 340784652.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

CASL-W60-64-frames Landmarks
---
license: mit
task_categories:
- video-classification
language:
- en
tags:
- sign-language
- mediapipe
- skeleton
- Central-African-Sign-Language
pretty_name: CASL-W60 Landmarks
size_categories:
- 1K<n<10K
---

# Dataset Card for CASL-W60 Landmarks

This dataset contains preprocessed holistic landmarks for **Central African Sign Language (CASL)**. It is specifically designed for training sequence-based models like Transformers or LSTMs for Sign Language Recognition (SLR).

## Dataset Details

- **Total Classes:** 60 (Signs)
- **Data Format:** NumPy arrays (.npy) converted to Parquet
- **Sequence Length:** 64 frames (fixed)
- **Features per Frame:** 225 landmarks (Pose + Left Hand + Right Hand)

### Dataset Structure

Each sample in the dataset contains:
* `landmarks`: A 2D array of shape `(64, 225)`.
* `label`: The string ID of the sign (e.g., `G003`).
* `split`: Whether the sample belongs to the `train` or `test` set.

## Feature Mapping (The 225 Vector)

The 225-dimensional vector for each frame is structured as follows:
1. **Pose (Indices 0-98):** 33 landmarks $\times$ 3 coordinates (X, Y, Z)
2. **Left Hand (Indices 99-161):** 21 landmarks $\times$ 3 coordinates (X, Y, Z)
3. **Right Hand (Indices 162-224):** 21 landmarks $\times$ 3 coordinates (X, Y, Z)



## Usage

You can load this dataset directly using the Hugging Face `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("luciayen/your-dataset-name")

# Access a sample
sample = dataset['train'][0]
print(sample['landmarks'])
print(sample['label'])