grad-cam 1.0.0

Grad-CAM visualization tool for PyTorch models.

Grad-CAM, Grad-CAM++ and Score-CAM implementation in Pytorch

What makes the network think the image label is 'pug, pug-dog' and 'tabby, tabby cat':

Combining Grad-CAM with Guided Backpropagation for the 'pug, pug-dog' class:

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]

---

Using from code

pip install pytorch-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)

---

Running the example script:

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

---

Using GradCAM++ or Score-CAM instead of GradCAM:

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
Resnet50

---

References

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