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View Code? Open in Web Editor NEWA PyTorch implementation of Grad-CAM based on ICCV 2017 paper "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization"
A PyTorch implementation of Grad-CAM based on ICCV 2017 paper "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization"
Hi, @mingloo @jacobgil @jdecid @ChaiKnight @flyingpot
I have fine-tuned VGG19_bn on my custom image set.
Please guide how to modify the code to use my model.
Thank you!
Best.
@bemoregt.
Line 36 in f082a57
classes = F.sigmoid(feature) #feature shape=[1,1], classes shape=[1,1]
one_hot, _ = classes.max(dim=-1) # one_hot shape=[1]
self.model.zero_grad()
one_hot.backward()
This one_hot
is not the one hot label, I don't know it is correct or not,
In my experiment, there is a class without any activation hot map. Did you know why? Thanks
I tried the program on the resnet 34 architecture with 100 classes and it simply did not work.
Apparently, the matrix transpose operation could not be performed. The size mismatch was 512x1 to 512x100.
Hi, @leftthomas
As a result of classifying with Resnet, Accuarcy is over 99%. If you hit map the object area with gradCAM with that model file, it does not match exactly. Why?
it does not match exactly. Why?
It seems to be a problem of GradCAM rather than Resnet classification learning. The objects to be hit-mapped are not as local or blob like dogs or cats, but close to a long straight line. In this case, GradCAM seems to miss the object area. Have you experienced this?
For a well-trainedd Resnet34 model, how do you optimize GradCAM?
Thanks, in advance.
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