This repository contains the source code of CIRKD (Cross-Image Relational Knowledge Distillation for Semantic Segmentation).
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:
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.
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
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] \
--pretrained [your checkpoint path]/deeplabv3_resnet101_citys_best_model.pth
CUDA_VISIBLE_DEVICES=0,1,2,3 \
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] \
--save-pred \
--pretrained [your checkpoint path]/deeplabv3_resnet101_citys_best_model.pth
You can submit the resulting images to the Cityscapes test server.
The current codes have been reorganized and we have not tested them thoroughly. If you have any questions, please contact us without hesitation.
@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}
}