How to visualize class activation maps with Grad-Cam?

How to visualize class activation maps with Grad-Cam?

The output of Grad-CAM is a heatmap visualization for a given class label (either the top, predicted label or an arbitrary label we select for debugging). We can use this heatmap to visually verify where in the image the CNN is looking.

How is the gradcam function used in classification?

The gradCAM function computes the importance map by taking the derivative of the reduction layer output for a given class with respect to a convolutional feature map. For classification tasks, the gradCAM function automatically selects suitable layers to compute the importance map for.

How to calculate class discriminative localization map in gradcam?

To obtain the class discriminative localization map of width u and height v for any class c, we first compute the gradient of the score for the class c, yc (before the softmax) with respect to feature maps Ak of a convolutional layer.

How are gradients used in grad cam technique?

The Grad-CAM technique utilizes the gradients of the classification score with respect to the final convolutional feature map, to identify the parts of an input image that most impact the classification score. The places where this gradient is large are exactly the places where the final score depends most on the data.

How to obtain class activation heatmap for image classification?

Description: How to obtain a class activation heatmap for an image classification model. Adapted from Deep Learning with Python (2017). You can change these to another model. To get the values for last_conv_layer_name use model.summary () to see the names of all layers in the model.

How is Grad Cam explains the model’s outputs?

We will see how the grad cam explains the model’s outputs for a multi-label image. Let’s try an image with a cat and a dog together, and see how the grad cam behaves. We generate class activation heatmap for “chow,” the class index is 260 We generate class activation heatmap for “egyptian cat,” the class index is 285

How to get last Conv layer name in grad Cam?

To get the values for last_conv_layer_name use model.summary () to see the names of all layers in the model. We will see how the grad cam explains the model’s outputs for a multi-label image. Let’s try an image with a cat and a dog together, and see how the grad cam behaves. We generate class activation heatmap for “chow,” the class index is 260