segmentation

ADE20k Semantic segmentation with CSWin

Results and Models

Backbone	Method	pretrain	Crop Size	Lr Schd	mIoU	mIoU (ms+flip)	#params	FLOPs	config	model	log
CSWin-T	UPerNet	ImageNet-1K	512x512	160K	49.3	50.7	60M	959G	config	model	log
CSWin-S	UperNet	ImageNet-1K	512x512	160K	50.4	51.5	65M	1027G	config	model	log
CSWin-B	UperNet	ImageNet-1K	512x512	160K	51.1	52.2	109M	1222G	config	model	log

Getting started

1. Install the Swin_Segmentation repository and some required packages.

git clone https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation
bash install_req.sh

2. Move the CSWin configs and backbone file to the corresponding folder.

cp -r configs/cswin <MMSEG_PATH>/configs/
cp config/_base/upernet_cswin.py <MMSEG_PATH>/config/_base_/models
cp backbone/cswin_transformer.py <MMSEG_PATH>/mmseg/models/backbones/
cp mmcv_custom/checkpoint.py <MMSEG_PATH>/mmcv_custom/

3. Install apex for mixed-precision training

git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./

4. Follow the guide in mmseg to prepare the ADE20k dataset.

Fine-tuning

Command format:

tools/dist_train.sh <CONFIG_PATH> <NUM_GPUS> --options model.pretrained=<PRETRAIN_MODEL_PATH>

For example, using a CSWin-T backbone with UperNet:

bash tools/dist_train.sh \
    configs/cswin/upernet_cswin_tiny.py 8 \
    --options model.pretrained=<PRETRAIN_MODEL_PATH>

pretrained models could be found at main page.

More config files can be found at configs/cswin.

Evaluation

Command format:

tools/dist_test.sh  <CONFIG_PATH> <CHECKPOINT_PATH> <NUM_GPUS> --eval mIoU
tools/dist_test.sh  <CONFIG_PATH> <CHECKPOINT_PATH> <NUM_GPUS> --eval mIoU --aug-test

For example, evaluate a CSWin-T backbone with UperNet:

bash tools/dist_test.sh configs/cswin/upernet_cswin_tiny.py \ 
    <CHECKPOINT_PATH> 8 --eval mIoU

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Acknowledgment

This code is built using the mmsegmentation library, Timm library, the Swin repository.