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CascadePSP: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement\n\n[Ho Kei Cheng*](https://hkchengrex.github.io/), Jihoon Chung*, Yu-Wing Tai, Chi-Keung Tang\n\n[[arXiv]](https://arxiv.org/abs/2005.02551) [[PDF]](https://arxiv.org/pdf/2005.02551)\n\n[[Supplementary Information (Comparisons with DenseCRF included!)]](https://openaccess.thecvf.com/content_CVPR_2020/supplemental/Cheng_CascadePSP_Toward_Class-Agnostic_CVPR_2020_supplemental.pdf)\n\n[[Supplementary image results]](http://hkchengad.student.ust.hk/CascadePSP/CascadePSP-supp-images.pdf)\n\n![gif](https://imgur.com/AD68ES8.gif)\n\n## Introduction\n\nCascadePSP is a deep learning model for high-resolution segmentation refinement.\nThis repository contains our PyTorch implementation with both training and testing functionalities. We also provide the annotated UHD dataset **BIG** and the pretrained model.\n\nHere are some refinement results on high-resolution images.\n![teaser](docs/images/teaser.jpg)\n\n## Quick Start\n\nTested on PyTorch 1.0 -- though higher versions would likely work for inference as well.\n\nCheck out [this folder](https://github.com/hkchengrex/CascadePSP/tree/master/segmentation-refinement). We have built a pip package that can refine an input image with two lines of code.\n\nInstall with\n\n```bash\npip install segmentation-refinement\n```\n\nCode demo:\n\n```python\nimport cv2\nimport time\nimport matplotlib.pyplot as plt\nimport segmentation_refinement as refine\nimage = cv2.imread('test/aeroplane.jpg')\nmask = cv2.imread('test/aeroplane.png', cv2.IMREAD_GRAYSCALE)\n\n# model_path can also be specified here\n# This step takes some time to load the model\nrefiner = refine.Refiner(device='cuda:0') # device can also be 'cpu'\n\n# Fast - Global step only.\n# Smaller L -\u003e Less memory usage; faster in fast mode.\noutput = refiner.refine(image, mask, fast=False, L=900) \n\n# this line to save output\ncv2.imwrite('output.png', output)\n\nplt.imshow(output)\nplt.show()\n```\n\n## Network Overview\n\n### Global Step \u0026 Local Step\n\n| Global Step | Local Step |\n|:-:|:-:|\n| ![Global Step](docs/images/global.jpg) | ![Local Step](docs/images/local.jpg) |\n\n\n### Refinement Module\n\n![Refinement Module](docs/images/rm.png)\n\n## Table of Contents\n\nRunning:\n\n- [Installation](docs/installation.md)\n- [Training](docs/training.md)\n- [Testing on Semantic Segmentation](docs/testing_segmentation.md)\n- [Testing on Scene Parsing](docs/testing_scene_parsing.md)\n\nDownloads:\n\n- [Pretrained Models](docs/models.md)\n- [BIG Dataset and Relabeled PASCAL VOC 2012](docs/dataset.md)\n\n## More Results\n\n### Refining the masks of Human 3.6M\n\n| Image | Original Mask | Refined Mask |\n|:-:|:-:|:-:|\n| ![Image](docs/images/H36M/test_im1.jpg) | ![OriginalMask](docs/images/H36M/test_mask1.jpg) | ![RefinedMask](docs/images/H36M/output1.jpg) |\n| ![Image](docs/images/H36M/test_im2.jpg) | ![OriginalMask](docs/images/H36M/test_mask2.jpg) | ![RefinedMask](docs/images/H36M/output2.jpg) |\n| ![Image](docs/images/H36M/test_im3.jpg) | ![OriginalMask](docs/images/H36M/test_mask3.jpg) | ![RefinedMask](docs/images/H36M/output3.jpg) |\n\nThe first row is the failure case (see neck).\n\n## Credit\n\nPSPNet implementation: https://github.com/Lextal/pspnet-pytorch\n\nSyncBN implementation: https://github.com/vacancy/Synchronized-BatchNorm-PyTorch\n\nIf you find our work useful in your research, please cite the following:\n\n```bibtex\n@inproceedings{cheng2020cascadepsp,\n  title={{CascadePSP}: Toward Class-Agnostic and Very High-Resolution Segmentation via Global and Local Refinement},\n  author={Cheng, Ho Kei and Chung, Jihoon and Tai, Yu-Wing and Tang, Chi-Keung},\n  booktitle={CVPR},\n  year={2020}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhkchengrex%2Fcascadepsp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fhkchengrex%2Fcascadepsp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fhkchengrex%2Fcascadepsp/lists"}