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: