Commit History

22 days ago

  1. Add stgcn_target_v1_encoder.npy (final target-pose ST-GCN, copy of runs/cueT2_stgcn_all/stgcn_label_encoder_v2.npy); new name only

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  2. Add stgcn_target_v1.pth (final target-pose ST-GCN, copy of runs/cueT2_stgcn_all/stgcn_sequence_model_v2.pth); new name only

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23 days ago

  1. add mlp_3head_gate_v1_encoder.npy (pose-cascade gate; new name, nothing replaced)

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  2. add mlp_3head_gate_v1.pth (pose-cascade gate; new name, nothing replaced)

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  1. MLP v4: 47.8% macro on frozen 103 photos (vs 35.5% live), all 3 heads trained

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  2. MLP v4: 47.8% macro on frozen 103 photos (vs 35.5% live), all 3 heads trained

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  1. MLP trained on real photos: 35.5% vs 14.1% macro on held-out photos

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  2. MLP trained on real photos: 35.5% vs 14.1% macro on held-out photos

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  3. Transition-aware ST-GCN: 63.0% macro/24-class eval, production key naming

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  4. Transition-aware ST-GCN: 63.0% macro/24-class eval, production key naming

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  1. zero_z-retrained MLP (real-world 52.6% vs ~0-6% baseline)

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  1. Upload stgcn_sequence_model_v2.pth with huggingface_hub

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  4. Upload stgcn_sequence_model_v2.pth with huggingface_hub

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  39. Upload mlp_3head_model_v2.pth with huggingface_hub

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