gradcam's Introduction

GradCAM

Tensorflow 2.0 implementation of GradCAM

This is a GradCAM implementation of pretrained models and also custom trained model

Instructions

Select any image and run:

    $ python pretrained_gradcam.py --image images/rugby.jpeg

Pretrained Models

All pretrained models listed below -

 - VGG16 
 - VGG19
 - ResNet50
 - InceptionV3 
 - InceptionResNetV2
 - Xception
 - MobileNet
 - MobileNetV2
 - DenseNet
 - NASNet
 - EfficientNet | Link - https://github.com/qubvel/efficientnet
 
 > NOTE - You need to install `EfficientNet` seperately from the given link because 
   it's not included in keras application.

You can choose any of the above models, default VGG16:

    $ python pretrained_gradcam.py --image images/rugby.jpeg --model VGG16

For specific layer GradCAM run below command:

> model.summary()

Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         

....
....

Selected the first conv layer of VGG16:

    $ python pretrained_gradcam.py --image images/rugby.jpeg --model VGG16 --layer block1_conv1

Custom Model

Run this command to work with your own trained model:

    $ python custom_gradcam.py --image [image] --model [model] --width [w] --height [h] --layer [layer]

Reference

Grad-CAM: Visualize class activation maps with Keras, TensorFlow, and Deep Learning @pyimagesearch

Thanks ❤️ @jrosebr1

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gradcam's Issues

Why the guided gradients?

Hi, could you explain the use of the guided gradients (below) with respect to the Selvaraju et al. 2019 paper? Do these guided gradients refer to the Guided Backpropagation part? I also don't see you using ReLU at any point?

# compute the guided gradients
castConvOutputs = tf.cast(convOutputs > 0, "float32")
castGrads = tf.cast(grads > 0, "float32")
guidedGrads = castConvOutputs * castGrads * grads

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