docs/phases/phase-2-data-and-model.md

Phase 2: WLASL Data And CNN Baseline

Goal

Move from placeholder demos to a real video-based classifier using WLASL and a landmark CNN baseline.

Deliverables

• finalize WLASL-100 as the main training dataset
• define the WLASL-25 subset used for live demo and VLM reranking
• extract fixed-length landmark sequences from labeled clips
• implement and train the temporal CNN baseline
• save model artifacts and label metadata for local and Hugging Face use
• document dataset licensing and keep large raw downloads out of Git

Implementation Notes

• Use MediaPipe Holistic to convert each clip into a (32, 225) landmark tensor.
• Train the CNN baseline first, then derive the WLASL-25 demo checkpoint.
• Reuse the same WLASL-25 clips for the CNN versus VLM comparison.

Exit Criteria

• the repo contains a stable WLASL evaluation manifest
• the CNN dry-run works locally
• the trained checkpoint loads in the Gradio app
• the team can explain why WLASL matches the final project scope