• 2020/01/13 The source code for reproduced HRNet+OCR has been made public.

• 2020/01/09 "HRNet + OCR + SegFix" achieves Rank#1 on Cityscapes leaderboard with mIoU as 84.5%.

• 2020/01/07 "HRNet+OCR[Mapillary+Coarse]" currently achieves 84.26% on Cityscapes test with better Mapillary pretraining, where we pretrain the HRNet+OCR model on the original Mapillary training set and achieve 50.8% on Mapillary val set. We can expect higher performance with various improvements, e.g., ASP-OCR, larger batch size/crop size (as in Panoptic-DeepLab) and our novel post-processing mechanism.

• 2020/01/03 "HRNet+OCR" will be made open-source in the code-base HRNet-Semantic-Segmentation very soon, thanks for your patience.

• 2020/01/02 Please email us ([email protected]) if you need the code for our OCR module and we would like to share it with you ASAP. We also hope you could try our method in your own code base and share the results with us.

• 2019/11/19 We have updated the paper OCR. Our approach achieves 83.7% and we can further achieve 84.0% on Cityscapes test set with a novel yet simple model-agnostic post-processing scheme. Our model-agnostic post-processing scheme is a new work under progress, which can be applied to improve the results of any existing approaches without any re-training or fine-tuning.

• 2019/09/25 We have released the paper OCR, which is method of our Rank#2 entry to the leaderboard of Cityscapes.

• 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.

• 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.

• 2019/06/19 We achieve 83.3%+ on the leaderboard of Cityscapes test set based on single model HRNetV2 + OCR. Cityscapes leaderboard We achieve 56.02% on the leaderboard of ADE20K test set based on single model ResNet101 + OCR without any bells or whistles. ADE20K leaderboard

• 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.

Please consider citing our work if you find it helps you,

@article{yuan2018ocnet,
  title={Ocnet: Object context network for scene parsing},
  author={Yuan Yuhui and Wang Jingdong},
  journal={arXiv preprint arXiv:1809.00916},
  year={2018}
}

@article{huang2019isa,
  title={Interlaced Sparse Self-Attention for Semantic Segmentation},
  author={Huang Lang and Yuan Yuhui and Guo Jianyuan and Zhang Chao and Chen Xilin and Wang Jingdong},
  journal={arXiv preprint arXiv:1907.12273},
  year={2019}
}

@article{yuan2019ocr,
  title={Object-Contextual Representations for Semantic Segmentation},
  author={Yuan Yuhui and Chen Xilin and Wang Jingdong},
  journal={arXiv preprint arXiv:1909.11065},
  year={2019}
}

• Cityscapes (testing with single scale whole image)

• ADE20K (testing with single scale whole image)

• LIP (testing with single scale whole image + left-right flip)

• Pascal-VOC (testing with single scale whole image)

• Pascal-Context (testing with single scale whole image)

• COCO-Stuff-10K (testing with single scale whole image)

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.