Comments (3)
Hey,
first of all load your own Model instead of VGG.
2. Apply your own Preprocessing depending on Model.
3. In modify_backprop re-instanciate your own Model.
4. in gradcam() resize the heatmap to you input size.
5. In compile_saliency_function exchange your layer of interest.
At last, take care of deprocessing.
Have fun
from keras-grad-cam.
@jenskrauth @abhigoku10 i tried to implement Grad-CAM for my own model. i couldn't, i got errors. do you have any code so that i can take it as a reference
from keras-grad-cam.
@navraj432 @abhigoku10
Do you understand how the process is going?Grad-Cam for custom defined architecture
from keras-grad-cam.
Related Issues (20)
- Regarding the gradient
- ValueError: Tried to convert 'x' to a tensor and failed. Error: None values not supported. HOT 9
- 'Node' object has no attribute 'output_masks' HOT 2
- saliency is NaN for VGG16 like model with BatchNorm HOT 1
- It does not match exactly. Why?
- AttributeError: Layer vgg16 has multiple inbound nodes, hence the notion of "layer input" is ill-defined. Use `get_input_at(node_index)` instead. HOT 7
- 3D images HOT 2
- zero mean intensity of gradient for some cases HOT 9
- high accuracy model with weak heatmap
- I feel using the gradient of last conv layer rule is more reasonable
- How can i use it with fully convolutional network??
- Running with cifar10 datset
- You must feed a value for placeholder tensor 'input_1_1' with dtype float and shape [?,299,299,3] HOT 1
- Apply GradCam to Cnn+LSTM HOT 2
- GradCam calculation
- 环境配置
- question: why replace keras.activations.relu to tf.nn.relu
- Requesting help with GradCam on Segmentation
- Grad-CAM for timeseries custom architecture
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from keras-grad-cam.