Cross-Image Relational Knowledge Distillation for Semantic Segmentation

This repository contains the source code of CIRKD (Cross-Image Relational Knowledge Distillation for Semantic Segmentation).

Requirement

Ubuntu 18.04 LTS

Python 3.8 (Anaconda is recommended)

CUDA 11.1

PyTorch 1.8.0

NCCL for CUDA 11.1

Backbones pretrained on ImageNet:

• resnet101-imagenet.pth
• resnet18-imagenet.pth
• mobilenetv2-imagenet.pth

Performance on Cityscapes

All models are trained over 8 * NVIDIA GeForce RTX 3090

Role	Network	Method	Val mIoU	test mIoU	Pretrained	train script
Teacher	DeepLabV3-ResNet101	-	78.07	77.46	Google Drive	sh
Student	DeepLabV3-ResNet18	Baseline	74.21	73.45	-	sh
Student	DeepLabV3-ResNet18	CIRKD	76.38	75.05	Google Drive	sh
Student	DeepLabV3-ResNet18*	Baseline	65.17	65.47	-	sh
Student	DeepLabV3-ResNet18*	CIRKD	68.18	68.22	Google Drive	sh
Student	DeepLabV3-MobileNetV2	Baseline	73.12	72.36	-	sh
Student	DeepLabV3-MobileNetV2	CIRKD	75.42	74.03	Google Drive	sh
Student	PSPNet-ResNet18	Baseline	72.55	72.29	-	sh
Student	PSPNet-ResNet18	CIRKD	74.73	74.05	Google Drive	sh

*denotes that we do not initialize the backbone with ImageNet pre-trained weights.

Evaluate pre-trained models on Cityscapes val and test sets

Evaluate the pre-trained models on val set

python -m torch.distributed.launch --nproc_per_node=8 eval.py \
    --model deeplabv3 \
    --backbone resnet101 \
    --data [your dataset path]/cityscapes/ \
    --save-dir [your directory path to store log files] \
    --gpu-id 0,1,2,3,4,5,6,7 \
    --pretrained [your checkpoint path]/deeplabv3_resnet101_citys_best_model.pth

Generate the resulting images on test set

python -m torch.distributed.launch --nproc_per_node=4 test.py \
    --model deeplabv3 \
    --backbone resnet101 \
    --data [your dataset path]/cityscapes/ \
    --save-dir [your directory path to store resulting images] \
    --gpu-id 0,1,2,3 \
    --save-pred \
    --pretrained [your checkpoint path]/deeplabv3_resnet101_citys_best_model.pth

You can submit the resulting images to the Cityscapes test server.

Citation

@inproceedings{yang2022cross,
  title={Cross-Image Relational Knowledge Distillation for Semantic Segmentation},
  author={Chuanguang Yang, Helong Zhou, Zhulin An, Xue Jiang, Yongjun Xu, Qian Zhang},
  booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year={2022}
}