PyTorch Knowledge distillation using Pspnet

This repository is a PyTorch implementation for Knowledge distillation. The code is modified from PSPNet. Sample experimented datasets is Cityscapes.

Highlight:
- All initialization models, trained models and predictions are available.
Requirement:
- PyTorch>=1.1.0, Python3, tensorboardX,

Clone the repository:

git clone https://github.com/LiuZhenshun/Knowledge-Distillation.git

Train:
- Download related datasets and symlink the paths to them as follows (you can alternatively modify the relevant paths specified in folder config):
```
cd Knowledge Distillation
mkdir -p dataset
ln -s /path_to_cityscapes_dataset dataset/
```
- Download initmodel/resnet50_v2 && exp/cityscapes/pspnet50 pre-trained models and put them under folder initmodel for weight initialization. Remember to use the right dataset format detailed in FAQ.md.
- Specify the gpu used in config then do training:
Visualization: tensorboardX incorporated for better visualization.
```
tensorboard --logdir=exp/cityscapes
```
result:
- Pspnet Result
- Kowlege Distillation Result

LiuZhenshun/KnowledgeDistillation