Gradient Class Activation Map (Grad-CAM) for a particular category indicates the discriminative image regions used by the CNN to identify that category.
The goal of this blog is to:
- understand concept of Grad-CAM
- understand Grad-CAM is generalization of CAM
- understand how to use it using keras-vis
- implement it using Keras's backend functions.
Image Specific Class Saliency Visualization allows better understanding of why a model makes a classification decision. The goal of this blog is to understand its concept and how to interpret the Saliency Map.
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Reference in this blog¶
In the next few blog posts, I will review visualization techiniques.
Image Specific Visualization¶
Create condo environment¶
conda create -n explainableAI python=3.5 source activate explainableAI conda install tensorflow conda install keras conda install jupyter pip install git+https://github.com/raghakot/keras-vis.git --upgrade --no-deps pip install opencv-python==184.108.40.206 $CONDA_PREFIX/bin/jupyter notebook --no-browser
Download a json file containing ImageNet class names.¶
This is the first blog post of Object Detection with YOLO blog series. The goal of this blog series is to understand the state-of-art object detection algorithm, called YOLO (you only look once). Allegedly, Andrew Ng mentioned that YOLO is the best object detection algorithm in his course. See the youtube video below: