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A large scale study of Knowledge Distillation.
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Distiller

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A clean Pytorch implementation to run quick distillation experiments. Our findings are available for reading in our paper "The State of Knowledge Distillation for Classification" linked here.

Python Dependencies

This codebase only supports Python 3.6+.

Required Python packages:

  • torch torchvision tqdm numpy pandas seaborn

All packages can be installed using pip3 install --user -r requirements.txt.

This project is also integerated with Pytorch Lightning. Use the lightning branch to see Pytorch Lightning compatible code.

Run

The benchmarks can be run via python3 evaluate_kd.py and providing the respective command line parameters. For example:

python3 evaluate_kd.py --epochs 200 --teacher resnet18 --student resnet8 --dataset cifar10 --teacher-checkpoint pretrained/resnet18_cifar10_95260_parallel.pth --mode nokd kd

Runs basic student training and knowledge distillation for 200 epochs using a pretrained teacher. There are checkpoints of multiple models in the pretrained folder.

Supported distillation modes

Distillation Technique Mode Description
Baseline Accuracy nokd Plain training with no knowledge distillation.
Baseline Knowledge Distillation Accuracy kd Hinton loss to distill a student network.
Ensemble of Teachers allkd Distill from a list of teacher models and pick the best performing one.
Hyper parameter tuning of Hinton Loss kdparam Distill using varying combinations of temperature and alpha and pick the best performing combination.
Triplet Loss triplet Knowledge Distillation with a triplet loss using the student as negative example.
Ensemble of Students multikd Train a student under an ensemble of students that are picked from a list.
Unsupervised Data Augmentation Loss uda Run knowledge distillation in combination with unsupervised data augmentation.
Teacher Assistant Knowledge Distillation takd Run distillation using Teacher-Assistant distillation.
Activation Boundary Distillation ab Run feature distillation using Activation-Boundary distillation.
Overhaul Distillation oh Run feature distillation using the Feature Overhaul distillation.
Relational Knowledge Distillation rkd Run distillation using the Relational Knowledge distillation.
Patient Knowledge Distillation pkd Run feature distillation using the Patient Knowledge distillation.
Simple Knowledge Distillation sfd Runs a custom feature distillation distillation (Simple Feature Distillation) that just pools and flattens feature layers.

Results

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