Image Classifier Using Pre-Trained Models in Keras

Train, predict, visualize, and produce class-activation map animations for deep learning models in Keras using pre-trained models as their basis.

Dependencies

• Python 3.5+
• Imagemagick 7+

Running the Example

1. Download the example dataset

2. Preprocess the data

python convert_data.py --data-dir {path-to-data}

3. Train the model

python train.py --pretrained_model {model} \
                --data-dir {path-to-data} \
                --weight-directory {path-to-weight-directory} \
                --tensorboard-directory {path-to-tensorboard-logdir} \
                --epochs {max_epochs}

4. Visualize model predictions

python visualize.py --weight-file {path-to-weight-file} \
                    --data-directory {path-to-data} \
                    --output-directory {path-to-output-directory} \
                    --image-path {path-to-image-to-visualize}

5. Generate a CAM plot

python cam_animation.py --weight-directory {path-to-weight-directory} \
                        --data-directory {path-to-data-directory} \
                        --image-path {path-to-image-to-visualize} \
                        --cam-path {output-path-for-cam-images} \
                        --weight-limit {max-weights-to-plot}

convert -delay 30 -size 256x256 {output-path-for-cam-images}/*.png -loop 0 {final-gif-name}

To make the generation of CAM plots easier, you can use the ./generate_cam_gifs script. This assumes:

• Data directory is ../data_dir/simpsons_dataset
• Weight directory is ../data_dir/weights
• CAM output path is ../data_dir/cam_output/{model}/{character}
• All names passed into the script are basenames

# Generate a single CAM plot
./generate_cam_gifs {model} {character} {npz-file}
# Generate CAM plots for the first 100 images of a character
./generate_cam_gifs {model} {character}