--- language: - en license: mit tags: - gesture-recognition - hand-gesture - pytorch - mediapipe - temporal-model - lstm - attention - bidirectional datasets: - IPN-Hand metrics: - accuracy - f1 model-index: - name: two_stream_attn_v1_finetune_20260510T160508Z results: - task: type: gesture-recognition dataset: name: IPN Hand type: IPN-Hand metrics: - type: accuracy value: 0.8623 - type: f1 value: 0.8577 --- # two_stream_attn_v1_finetune_20260510T160508Z A real-time hand gesture classifier trained on the [IPN Hand dataset](https://gibranbenitez.github.io/IPN_Hand/). This model is part of the **Maestro** pipeline that enables touchless control of presentation and meeting software through hand gestures captured from a standard webcam using MediaPipe for landmark extraction. ## Model Description - **Architecture**: EnhancedTwoStreamLSTM (BiLSTM h=128×2, MHA 8 heads, proj=96, mean+max pool, MLP gate) - **Parameters**: 2,099,434 - **Input**: `(batch, 32, 147)` — 32-frame sliding window at 30 FPS ≈ 1067 ms - **Output**: Softmax logits over 10 gesture classes - **Inference latency**: =0.10.14** for landmark extraction at inference time. - Not intended for safety-critical or accessibility-critical applications. - Performance was measured on a held-out test split from the same dataset; real-world generalisation may differ. ## Environmental Impact Training was performed on CPU/MPS. Estimated training time: ~10 minutes. Estimated CO2 equivalent: negligible (<0.001 kg CO2eq). --- *Generated by the Maestro training pipeline on 2026-05-10.*
