Hello ,
base_model, preprocess_input = Classifiers.get('seresnext50')
model = base_model(input_shape=(512, 512, 20, 1 ), weights=None , include_top = False )
x = Flatten()(model.output)
x = Dense(1024, activation= 'sigmoid')(x)
x = Dense(2, activation= 'sigmoid')(x)
Trying to train a model , the accuracy is everything resides upto expectation, but the gradcam are quite off from the region of the focus - how the accuracy is good but the grad cam is off the focus of targeted area .
Using the layer - 'activation-161' as output
ref - https://github.com/fitushar/3D-Grad-CAM/blob/master/3DGrad-CAM.ipynb for the gradcam generation code , the results are always at the border of the image.
Hello ,
base_model, preprocess_input = Classifiers.get('seresnext50')
model = base_model(input_shape=(512, 512, 20, 1 ), weights=None , include_top = False )
x = Flatten()(model.output)
x = Dense(1024, activation= 'sigmoid')(x)
x = Dense(2, activation= 'sigmoid')(x)
Trying to train a model , the accuracy is everything resides upto expectation, but the gradcam are quite off from the region of the focus - how the accuracy is good but the grad cam is off the focus of targeted area .
Using the layer - 'activation-161' as output
ref - https://github.com/fitushar/3D-Grad-CAM/blob/master/3DGrad-CAM.ipynb for the gradcam generation code , the results are always at the border of the image.