openseg.pytorch
News
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2021/09/14 MMSegmentation has supported our ISANet and refer to ISANet for more details.
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2021/08/13 We have released the implementation for HRFormer and the combination of HRFormer and OCR achieves better semantic segmentation performance.
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2021/03/12 The late ACCPET is finally here, our "OCNet: Object context network for scene parsing" has been accepted by IJCV-2021, which consists of two of our previous technical reports: OCNet and ISA. Congratulations to all the co-authors!
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2021/02/16 Support pytorch-1.7, mixed-precision, and distributed training. Based on the PaddleClas ImageNet pretrained weights, we achieve 83.22% on Cityscapes val, 59.62% on PASCAL-Context val (new SOTA), 45.20% on COCO-Stuff val (new SOTA), 58.21% on LIP val and 47.98% on ADE20K val. Please checkout branch pytorch-1.7 for more details.
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2020/12/07 PaddleSeg has supported our ISA and HRNet + OCR. Jittor also has supported our ResNet-101 + OCR.
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2020/08/16 MMSegmentation has supported our HRNet + OCR.
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2020/07/20 The researchers from AInnovation have achieved Rank#1 on ADE20K Leaderboard via training our HRNet + OCR with a semi-supervised learning scheme. More details are in their Technical Report.
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2020/07/09 OCR (Spotlight) and SegFix have been accepted by the ECCV-2020. Notably, the reseachers from Nvidia set a new state-of-the-art performance on Cityscapes leaderboard: 85.4% via combining our HRNet + OCR with a new hierarchical mult-scale attention scheme.
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2020/05/11 We have released the checkpoints/logs of "HRNet + OCR" on all the 5 benchmarks including Cityscapes, ADE20K, LIP, PASCAL-Context and COCO-Stuff in the Model Zoo. Please feel free to try our method on your own dataset.
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2020/04/18 We have released some of our checkpoints/logs of OCNet, ISA, OCR and SegFix. We highly recommend you to use our SegFix to improve your segmentation results as it is super easy & fast to use.
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2020/03/12 Our SegFix could be used to improve the performance of various SOTA methods on both semantic segmentation and instance segmentation, e.g., "PolyTransform + SegFix" achieves Rank#2 on Cityscapes leaderboard (instance segmentation track) with performance as 41.2%.
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2020/01/13 The source code for reproduced HRNet+OCR has been made public.
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2020/01/09 "HRNet + OCR + SegFix" achieves Rank#1 on Cityscapes leaderboard with mIoU as 84.5%.
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2019/09/25 We have released the paper OCR, which is method of our Rank#2 entry to the leaderboard of Cityscapes.
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2019/07/31 We have released the paper ISA, which is very easy to use and implement while being much more efficient than OCNet or DANet based on conventional self-attention.
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2019/07/23 We (HRNet + OCR w/ ASP) achieve Rank#1 on the leaderboard of Cityscapes (with a single model) on 3 of 4 metrics.
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2019/05/27 We achieve SOTA on 6 different semantic segmentation benchmarks including: Cityscapes, ADE20K, LIP, Pascal-Context, Pascal-VOC, COCO-Stuff. We provide the source code for our approach on all the six benchmarks.
Model Zoo and Baselines
We provide a set of baseline results and trained models available for download in the Model Zoo.
Introduction
This is the official code of OCR, OCNet, ISA and SegFix. OCR, OCNet, and ISA focus on better context aggregation mechanisms (in the semantic segmentation task) and ISA focuses on addressing the boundary errors (in both semantic segmentation and instance segmentation tasks). We highlight the overall framework of OCR and SegFix in the figures as shown below:
Citation
Please consider citing our work if you find it helps you,
@article{YuanW18,
title={Ocnet: Object context network for scene parsing},
author={Yuhui Yuan and Jingdong Wang},
journal={arXiv preprint arXiv:1809.00916},
year={2018}
}
@article{HuangYGZCW19,
title={Interlaced Sparse Self-Attention for Semantic Segmentation},
author={Lang Huang and Yuhui Yuan and Jianyuan Guo and Chao Zhang and Xilin Chen and Jingdong Wang},
journal={arXiv preprint arXiv:1907.12273},
year={2019}
}
@article{YuanCW20,
title={Object-Contextual Representations for Semantic Segmentation},
author={Yuhui Yuan and Xilin Chen and Jingdong Wang},
journal={arXiv preprint arXiv:1909.11065},
year={2020}
}
@article{YuanXCW20,
title={SegFix: Model-Agnostic Boundary Refinement for Segmentation},
author={Yuhui Yuan and Jingyi Xie and Xilin Chen and Jingdong Wang},
journal={arXiv preprint arXiv:2007.04269},
year={2020}
}
@article{YuanFHZCW21,
title={HRT: High-Resolution Transformer for Dense Prediction},
author={Yuhui Yuan and Rao Fu and Lang Huang and Weihong Lin and Chao Zhang and Xilin Chen and Jingdong Wang},
booktitle={arXiv preprint arXiv:2110.09408},
year={2021}
}
Acknowledgment
This project is developed based on the segbox.pytorch and the author of segbox.pytorch donnyyou retains all the copyright of the reproduced Deeplabv3, PSPNet related code.
and so on,
environment should be satisfied：
and the config.profile:
this is my log file screenshot:
In inference, I have printed 10 random rows in the 
All values in this map are very close to 0.0526 (equals to 1/19).
Coarse Label Map，Offset Map，Refined Label Map，Distance Map， Direction Map and the last one，How to draw them。Which drawing software is used, which is a program, what is the name of the software, and can the program be open source?I want to apply Figure 2 and Figure 3 to my own grayscale map. If it can be open sourced, will it be possible in the near future?Thanks you very much.
您好，抱歉我的英语太渣了，想了解一下这3张图是如何制作的。哪些图用了画图软件，是什么软件，哪些用了程序，程序可以开源吗。我想把图2和图3应用到自己的灰度图上，如果可以开源，近期可以吗？谢谢各位大佬，万分感谢。
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