Knowledge-Distillation(KD) is a simple way to compress model while keeping the performance of original model. This repository provide a implementation of the paper "Distilling the Knowledge in a Neural Network" with some changes. Please check the references for detailed explanations.

• python 3.6 >
• pytorch 1.3 >

.
|--experiments (it will include the scripts to run, results of train and test)
|--train_net.py (main solver to train the model)
|--data/
|    |--data_loader.py (Data queue module)
|--models/
|    |--Loss.py (Loss functions that are used in this project)
|--engine/
|    |--trainer.py
|    |--inference.py
|    |--solver.py
|--utils/
|    |--logger.py (module that can visualization of image and training plot)  
|    |--checkpointer.py
|    |--measure.py

You should make clear that all directory and files are located correctly

• Check all requirements to run
• Additional feature to improve baseline(Hinton's 15)
• Train teachers

I added two scripts. One for training the teacher model and another for trainingthe student using teacher.
Specify your teacher model in models and build_model if you have own your model and put arguments correctly to run.

$ ./experiments/exp1/train.sh

Table will be appear

$ tensorboard --logdir=experiment/ --port=6666

and go to 'localhost:6666' on webbrowser. You can see the accuracy and loss graph

• "Distilling the Knowledge in a Neural Network"
• "Python implementaion of hinton's KD"
  • I refered CS230 report to understand loss function(Cross entropy + KL_div)