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A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility
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| 2026-07-28 | 1997 |
| 2026-07-30 | 1997 |
| 2026-08-06 | 1997 |
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# knowledge-distillation-pytorch
* Exploring knowledge distillation of DNNs for efficient hardware solutions
* Author: Haitong Li
* Framework: PyTorch
* Dataset: CIFAR-10
## Features
* A framework for exploring "shallow" and "deep" knowledge distillation (KD) experiments
* Hyperparameters defined by "params.json" universally (avoiding long argparser commands)
* Hyperparameter searching and result synthesizing (as a table)
* Progress bar, tensorboard support, and checkpoint saving/loading (utils.py)
* Pretrained teacher models available for download
## Install
* Clone the repo
```
git clone https://github.com/peterliht/knowledge-distillation-pytorch.git
```
* Install the dependencies (including Pytorch)
```
pip install -r requirements.txt
```
## Organizatoin:
* ./train.py: main entrance for train/eval with or without KD on CIFAR-10
* ./experiments/: json files for each experiment; dir for hypersearch
* ./model/: teacher and student DNNs, knowledge distillation (KD) loss defination, dataloader
## Key notes about usage for your experiments:
* Download the zip file for pretrained teacher model checkpoints from ["experiments.zip"](https://drive.google.com/file/d/12slKl4Vc8SbozFlvb-ahoR95F5yCwB_K/view?usp=sharing)
* Simply move the unzipped subfolders into 'knowledge-distillation-pytorch/experiments/' (replacing the existing ones if necessary; follow the default path naming)
* Call train.py to start training 5-layer CNN with ResNet-18's dark knowledge, or training ResNet-18 with state-of-the-art deeper models distilled
* Use search_hyperparams.py for hypersearch
* Hyperparameters are defined in params.json files universally. Refer to the header of search_hyperparams.py for details
## Train (dataset: CIFAR-10)
Note: all the hyperparameters can be found and modified in 'params.json' under 'model_dir'
-- Train a 5-layer CNN with knowledge distilled from a pre-trained ResNet-18 model
```
python train.py --model_dir experiments/cnn_distill
```
-- Train a ResNet-18 model with knowledge distilled from a pre-trained ResNext-29 teacher
```
python train.py --model_dir experiments/resnet18_distill/resnext_teacher
```
-- Hyperparameter search for a specified experiment ('parent_dir/params.json')
```
python search_hyperparams.py --parent_dir experiments/cnn_distill_alpha_temp
```
--Synthesize results of the recent hypersearch experiments
```
python synthesize_results.py --parent_dir experiments/cnn_distill_alpha_temp
```
## Results: "Shallow" and "Deep" Distillation
Quick takeaways (more details to be added):
* Knowledge distillation provides regularization for both shallow DNNs and state-of-the-art DNNs
* Having unlabeled or partial dataset can benefit from dark knowledge of teacher models
-**Knowledge distillation from ResNet-18 to 5-layer CNN**
| Model | Dropout = 0.5 | No Dropout |
| :------------------: | :----------------: | :-----------------:|
| 5-layer CNN | 83.51% | 84.74% |
| 5-layer CNN w/ ResNet18 | 84.49% | **85.69%** |
-**Knowledge distillation from deeper models to ResNet-18**
|Model | Test Accuracy|
|:--------: | :---------: |
|Baseline ResNet-18 | 94.175% |
|+ KD WideResNet-28-10 | 94.333% |
|+ KD PreResNet-110 | 94.531% |
|+ KD DenseNet-100 | 94.729% |
|+ KD ResNext-29-8 | **94.788%** |
## References
H. Li, "Exploring knowledge distillation of Deep neural nets for efficient hardware solutions," [CS230 Report](http://cs230.stanford.edu/files_winter_2018/projects/6940224.pdf), 2018
Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. "Distilling the knowledge in a neural network." arXiv preprint arXiv:1503.02531 (2015).
Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., & Bengio, Y. (2014). Fitnets: Hints for thin deep nets. arXiv preprint arXiv:1412.6550.
https://github.com/cs230-Excerpt of 4,085 characters
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matched fp:2d6c70545582e6c2, topic:model-compression, topic:knowledge-distillation, name:knowledge distillation