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Bottleneck Transformers for Visual Recognition
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leaderj1001/BottleneckTransformers
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- support Multi-head Attention
| Model | heads | Params (M) | Acc (%) |
|---|---|---|---|
| ResNet50 baseline (ref) | 23.5M | 93.62 | |
| BoTNet-50 | 1 | 18.8M | 95.11% |
| BoTNet-50 | 4 | 18.8M | 95.78% |
| BoTNet-S1-50 | 1 | 18.8M | 95.67% |
| BoTNet-S1-59 | 1 | 27.5M | 95.98% |
| BoTNet-S1-77 | 1 | 44.9M | wip |
- Model
frommodelimportModelmodel=ResNet50(num_classes=1000,resolution=(224,224))x=torch.randn([2,3,224,224])print(model(x).size())
- Module
frommodelimportMHSAresolution=14mhsa=MHSA(planes,width=resolution,height=resolution)
- Paper link
- Author: Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani
- Organization: UC Berkeley, Google Research
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