ResNet_Attention:ResNet +注意力

# ResNet_Attention(CBAM，SE) Official instruction: [CBAM](https://arxiv.org/abs/1807.06521)，[SE](https://arxiv.org/abs/1709.01507) # Required Environment Ubuntu20.04 GTX 1080Ti Python3.7 PyTorch1.7.0 CUDA10.2 CuDNN7.0 # Usage Method(trian with CIFAR10) The model's backbone is ResNet. In our training, we use CIFAR10 as our dataset. ``` # To train with Se python train_CIFAR10.py --prefix 4 --device 1 --epoch 160 --att_type se # To trian with CBAM python train_CIFAR10.py --prefix 5 --device 1 --epoch 160 --att_type cbam ``` # Validation Result * ResNet50 (trained for 160 epochs) ACC@1=93.41% ACC@5=99.84% * ResNet50+SE (trained for 160 epochs) ACC@1=94.01% ACC@5=99.89% # Result Graph **Blue：ResNet50** **Red：ResNet50+SE**