PKUSeg is an open source semantic segmentation toolbox based on PyTorch, which is maintained by EECS of Peking University. Maintainers are all from Key Laboratory of Machine Perception (MOE).
- Modular design and easy to use and deploy
We develop this tool for easier experiments and deployment． - All kinds of models for semantic segmentation
We implement many state-of-the-art models in research papers. We not only release codes, but also training checkpoints. - State-of-the-art results on multiple datasets
We achieve the state-of-the-art results on multiple datasets including Pascal VOC, Cityscapes, Pascal Context and ADE20K．
- PSPNet: Pyramid Scene Parsing Network CVPR2017
- DeepLabV3: Rethinking Atrous Convolution for Semantic Image Segmentation CVPR2017
- DeepLabV3+: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation ECCV2018
- DenseASPP: DenseASPP for Semantic Segmentation in Street Scenes CVPR2018
- DANet: Dual Attention Network for Scene Segmentation CVPR2019
- EMANet: Expectation-Maximization Attention Networks for Semantic Segmentation ICCV2019
All the performances showed below fully reimplemented the papers' results.
- Single Scale Whole Image Test: Base LR 0.01, Crop Size 513
| Model | Backbone | Train | Test | mIOU | BS | Iters | Scripts |
|---|---|---|---|---|---|---|---|
| PSPNet | 3x3-Res101 | train | val | 78.75 | 16 | 3W | PSPNet |
| DeepLabV3 | 3x3-Res101 | train | val | 78.95 | 16 | 3W | DeepLabV3 |
| EMANet | 3x3-Res101 | train | val | 79.79 | 16 | 3W | EMANet |
- 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 |
- 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 |
This project is released under the Apache 2.0 license.