Comments (1)
It is because of the nested layers, you have to select the conv layers within the residual layers. You can modify two or three lines of code and make it compatible with resnets or other non-vgg-like models. Check previously closed issues, if I remember correctly, there was some code that had resnet compatibility.
The reason it is not supported out of the box is because If I push some code that support resnets, there will be someone asking support for inceptions, then for densenet, then for vit, then for mxnet, then for deit. You see how this spirals out of control. Eventually, this repository is going to have more code about models than the techniques themselves.
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Related Issues (20)
- Why this cam-zoo don't have grad-cam++?May you add grad-cam++ in this project? HOT 1
- Support for LayerCAM HOT 4
- a question on "cam = np.ones(target.shape[1:], dtype=np.float32)" in gradcam.py HOT 2
- How to get the sampling points with deformable conv? HOT 1
- A question about the method to get output from specific layer HOT 4
- question on image generation
- Image Reconstruction size is same as conv1 layer HOT 1
- Visualizations for CNN trained on timeseries classification HOT 1
- Could you please provide the feature importance included dataset that has been generated? HOT 1
- AttributeError: 'MyCNN' object has no attribute 'features' HOT 1
- Extract gradient without model_output HOT 3
- GradCAM
- GradCAM HOT 1
- Why take np.maximum(cam, 0) in GradCam? HOT 1
- attention HOT 1
- Help with understanding layer backpropagation. HOT 1
- Can this tool be used on non-classification tasks HOT 1
- Code application related issues HOT 1
- RUN
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from pytorch-cnn-visualizations.