isolated & continuous sign language recognition using CNN+LSTM/3D CNN/GCN/Encoder-Decoder
- Download and extract CSL Dataset
- Download and install PyTorch
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four layers of Conv2d + one layer of LSTM
Dataset Classes Samples Best Test Acc Best Test Loss CSL_Isolated 100 25,000 82.08% 0.734426 CSL_Isolated 500 125,000 71.71% 1.332122 -
ResNet + one layer of LSTM
Dataset Classes Samples Best Test Acc Best Test Loss CSL_Isolated 100 25,000 93.54% 0.245582 CSL_Isolated 500 125,000 83.17% 0.748759
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three layers of Conv3d
Dataset Classes Samples Best Test Acc Best Test Loss CSL_Isolated 100 25,000 58.86% 1.560049 CSL_Isolated 500 125,000 45.07% 2.255563 -
3D ResNet
Method Dataset Classes Samples Best Test Acc Best Test Loss ResNet18 CSL_Isolated 100 25,000 93.30% 0.246169 ResNet18 CSL_Isolated 500 125,000 79.42% 0.800490 ResNet34 CSL_Isolated 100 25,000 94.78% 0.207592 ResNet34 CSL_Isolated 500 125,000 81.61% 0.750424 ResNet50 CSL_Isolated 100 25,000 94.36% 0.232631 ResNet50 CSL_Isolated 500 125,000 83.15% 0.803212 ResNet101 CSL_Isolated 100 25,000 95.26% 0.205430 ResNet101 CSL_Isolated 500 125,000 83.18% 0.751727 -
ResNet (2+1)D
Dataset Classes Samples Best Test Acc Best Test Loss CSL_Isolated 100 25,000 98.68% 0.043099 CSL_Isolated 500 125,000 94.85% 0.234880
Dataset | Classes | Samples | Best Test Acc | Best Test Loss |
---|---|---|---|---|
CSL_Skeleton | 100 | 25,000 | 79.20% | 0.737053 |
CSL_Skeleton | 500 | 125,000 | 66.64% | 1.165872 |
Dataset | Classes | Samples | Best Test Acc | Best Test Loss |
---|---|---|---|---|
CSL_Skeleton | 100 | 25,000 | 84.30% | 0.488253 |
CSL_Skeleton | 500 | 125,000 | 70.62% | 1.078730 |
Encoder is ResNet18+LSTM, and Decoder is LSTM
Dataset | Sentences | Samples | Best Test Wer | Best Test Loss |
---|---|---|---|---|
CSL_Continuous | 100 | 25,000 | 1.01% | 0.034636 |
CSL_Continuous_Char | 100 | 25,000 | 1.19% | 0.049449 |