This project is an open source semantic segmentation toolbox based on PyTorch. It is based on the codes of our Tianchi competition in 2021 (https://tianchi.aliyun.com/competition/entrance/531860/introduction).
In the competition, our team won the third place (please see Tianchi_README.md).

The master branch works with PyTorch 1.6+.The project now supports popular and contemporary semantic segmentation frameworks, e.g. UNet, DeepLabV3+, HR-Net etc.

• ResNet (CVPR'2016)
• SeNet (CVPR'2018)
• IBN-Net (CVPR'2018)
• EfficientNet (CVPR'2020)

• UNet
• DLink-Net
• Res-UNet
• Efficient-UNet
• Deeplab v3+
• HR-Net

• MixUp /CutMix /CopyPaste
• SWA
• LovaszSoftmax Loss /LargeMarginSoftmax Loss
• FP16
• Multi-scale

• large image inference (cut and merge)
• post process (crf/superpixels)

python train.py --config_file ${CONFIG_FILE} 

• CONFIG_FILE: File of training config about model

Examples:
We trained our model in Tianchi competition according to the following script:
Stage 1 (160e)

python train.py --config_file configs/tc_seg/tc_seg_res_unet_r34_ibn_a_160e.yml

Stage 2 (swa 24e)

python train.py --config_file configs/tc_seg/tc_seg_res_unet_r34_ibn_a_swa.yml

python inference.py --config_file ${CONFIG_FILE} 

• CONFIG_FILE: File of inference config about model

python predict_demo.py --config_file ${CONFIG_FILE} --rs_img_file ${IMAGE_FILE_PATH} --temp_img_save_path ${TEMP_CUT_PATH} -temp_seg_map_save_path ${TEMP_SAVE_PATH} --save_seg_map_file ${SAVE_SEG_FILE} 

• CONFIG_FILE: File of inference config about model
• IMAGE_FILE_PATH: File of large input image to predict
• TEMP_CUT_PATH: Temp folder of small cutting samples
• TEMP_SAVE_PATH: Temp folder of predict results of cutting samples
• SAVE_SEG_FILE: Predict result of the large image