Gradient class activation maps are a visualization technique for deep learning networks.
See the paper: https://arxiv.org/pdf/1610.02391v1.pdf
The paper authors' torch implementation: https://github.com/ramprs/grad-cam
My Keras implementation: https://github.com/jacobgil/keras-grad-cam
Tested with most of the torchvision models. You need to choose the target layer to compute CAM for. Some common choices can be:
- Resnet18 and 50: model.layer4[-1]
- VGG and densenet161: model.features[-1]
- mnasnet1_0: model.layers[-1]
pip install grad-cam
from pytorch_grad_cam import CAM
from pytorch_grad_cam.utils.image import show_cam_on_image
from torchvision.models import resnet50
model = resnet50(pretrained=True)
target_layer = model.layer4[-1]
method = 'gradcam' # Can be gradcam/gradcam++/scorecam
input_tensor = # Create an input tensor image for your model..
cam = CAM(model=model, target_layer=target_layer, use_cuda=args.use_cuda)
grayscale_cam = cam(input_tensor=input_tensor, target_category=1, method=method)
visualization = show_cam_on_image(rgb_img, grayscale_cam)Usage: python gradcam.py --image-path <path_to_image> --method <method>
To use with CUDA:
python gradcam.py --image-path <path_to_image> --use-cuda
You can choose between:
method='gradcam'method='gradcam++'method='scorecam'
It seems that GradCAM++ is almost the same as GradCAM, in most networks except VGG where the advantage is larger.
| Network | Image | GradCAM | GradCAM++ | Score-CAM |
|---|---|---|---|---|
| VGG16 |  |
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| Resnet50 | |
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https://arxiv.org/abs/1610.02391
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, Dhruv Batra
https://arxiv.org/abs/1710.11063
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks Aditya Chattopadhyay, Anirban Sarkar, Prantik Howlader, Vineeth N Balasubramanian
https://arxiv.org/abs/1910.01279
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel, Xia Hu