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MT-Segmentation

@misc{mt-segmentation,
    author = {Ansheng You, Zhenhua Chai},
    title = {MT-Segmentation},
    howpublished = {\url{http://git.sankuai.com/users/youansheng/repos/mt-segmentation}},
    year = {2020}
}

This repository provides source code for most deep learning based cv problems. We'll do our best to keep this repository up-to-date. If you do find a problem about this repository, please raise an issue or submit a pull request.

Implemented Papers

  • Semantic Segmentation
    • DeepLabV3: Rethinking Atrous Convolution for Semantic Image Segmentation
    • PSPNet: Pyramid Scene Parsing Network
    • DenseASPP: DenseASPP for Semantic Segmentation in Street Scenes
    • Asymmetric Non-local Neural Networks for Semantic Segmentation

QuickStart with TorchCV

Now only support Python3.x, pytorch 1.3.

pip3 install -r requirements.txt
cd lib/exts
sh make.sh

Performances with MT-Segmentation

All the performances showed below fully reimplemented the papers' results.

Semantic Segmentation

  • Cityscapes (Single Scale Whole Image Test): Base LR 0.01, Crop Size 769
Model Backbone Train Test mIOU BS Iters Scripts
PSPNet 3x3-Res101 train val 78.20 8 4W PSPNet
DeepLabV3 3x3-Res101 train val 79.13 8 4W DeepLabV3
  • ADE20K (Single Scale Whole Image Test): Base LR 0.02, Crop Size 520
Model Backbone Train Test mIOU PixelACC BS Iters Scripts
PSPNet 3x3-Res50 train val 41.52 80.09 16 15W PSPNet
DeepLabv3 3x3-Res50 train val 42.16 80.36 16 15W DeepLabV3
PSPNet 3x3-Res101 train val 43.60 81.30 16 15W PSPNet
DeepLabv3 3x3-Res101 train val 44.13 81.42 16 15W DeepLabV3

Commands with MT-Segmentation

Take PSPNet as an example. ("tag" could be any string, include an empty one.)

  • Training
cd scripts/cityscapes/
bash run_fs_pspnet_cityscapes_seg.sh train tag
  • Resume Training
cd scripts/cityscapes/
bash run_fs_pspnet_cityscapes_seg.sh train tag
  • Validate
cd scripts/cityscapes/
bash run_fs_pspnet_cityscapes_seg.sh val tag
  • Testing:
cd scripts/cityscapes/
bash run_fs_pspnet_cityscapes_seg.sh test tag