README.md

Grad-CAM module

Grad-CAM pytorch implementation of original paper.

Basic Setups

Open config.py, and edit the lines below to your data directory.

name = [:The name of your dataset that you trained on module 3 (classifier)]
data_base = [:dir to your original dataset]
aug_base =  [:dir to your actually trained dataset]

For training, your data file system should be in the following hierarchy. Organizing codes for your data into the given requirements will be provided in the preprocessor module

[:data file name]

    |-train
        |-[:class 0]
        |-[:class 1]
        |-[:class 2]
        ...
        |-[:class n]
    |-val
        |-[:class 0]
        |-[:class 1]
        |-[:class 2]
        ...
        |-[:class n]

How to run

After you have cloned the repository, you can train the dataset by running the script below.

You can set the dimension of the additional layer in config.py

# grad-cam exploits
python launch_model --net_type [:net_type] --depth [:depth]

# For example, for the resnet-50 model I've trained, type
python launch_model --net_type resnet --depth 50

Test out various networks

Before testing out the networks, make sure that you have a trained weight obtained in the checkpoint file of the classifier module

Supporting networks

• AlexNet [:TODO]
• VGGNet [:TODO]
• ResNet

Results

• Original Image

• Grad-CAM Image

Attention for cat

Attention for dog