{"id":13788580,"url":"https://github.com/Eromera/erfnet_pytorch","last_synced_at":"2025-05-12T03:30:38.583Z","repository":{"id":202082660,"uuid":"104142994","full_name":"Eromera/erfnet_pytorch","owner":"Eromera","description":"Pytorch code for semantic segmentation using ERFNet","archived":false,"fork":false,"pushed_at":"2019-12-05T10:20:15.000Z","size":22664,"stargazers_count":429,"open_issues_count":14,"forks_count":125,"subscribers_count":15,"default_branch":"master","last_synced_at":"2024-11-18T02:38:10.286Z","etag":null,"topics":["cityscapes","erfnet","pytorch","segmentation","semantic","semantic-segmentation"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Eromera.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"license.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2017-09-20T00:07:04.000Z","updated_at":"2024-11-14T07:32:25.000Z","dependencies_parsed_at":"2024-04-07T21:31:49.901Z","dependency_job_id":null,"html_url":"https://github.com/Eromera/erfnet_pytorch","commit_stats":null,"previous_names":["eromera/erfnet_pytorch"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eromera%2Ferfnet_pytorch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eromera%2Ferfnet_pytorch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eromera%2Ferfnet_pytorch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Eromera%2Ferfnet_pytorch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Eromera","download_url":"https://codeload.github.com/Eromera/erfnet_pytorch/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253667920,"owners_count":21944939,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cityscapes","erfnet","pytorch","segmentation","semantic","semantic-segmentation"],"created_at":"2024-08-03T21:00:50.481Z","updated_at":"2025-05-12T03:30:33.567Z","avatar_url":"https://github.com/Eromera.png","language":"Python","funding_links":[],"categories":["2.) Lightweight Structures"],"sub_categories":["**[Papers]**"],"readme":"# ERFNet (PyTorch version)\n\nThis code is a toolbox that uses **PyTorch** for training and evaluating the **ERFNet** architecture for semantic segmentation.\n\n**For the Original Torch version please go [HERE](https://github.com/Eromera/erfnet)**\n\nNOTE: This PyTorch version has a slightly better result than the ones in the Torch version (used in the paper): 72.1 IoU in Val set and 69.8 IoU in test set.\n\n![Example segmentation](example_segmentation.png?raw=true \"Example segmentation\")\n\n## Publications\n\nIf you use this software in your research, please cite our publications:\n\n**\"Efficient ConvNet for Real-time Semantic Segmentation\"**, E. Romera, J. M. Alvarez, L. M. Bergasa and R. Arroyo, IEEE Intelligent Vehicles Symposium (IV), pp. 1789-1794, Redondo Beach (California, USA), June 2017. \n**[Best Student Paper Award]**, [[pdf]](http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17iv.pdf)\n\n**\"ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation\"**, E. Romera, J. M. Alvarez, L. M. Bergasa and R. Arroyo, Transactions on Intelligent Transportation Systems (T-ITS), December 2017. [[pdf]](http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf)\n\n## Packages\nFor instructions please refer to the README on each folder:\n\n* [train](train) contains tools for training the network for semantic segmentation.\n* [eval](eval) contains tools for evaluating/visualizing the network's output.\n* [imagenet](imagenet) Contains script and model for pretraining ERFNet's encoder in Imagenet.\n* [trained_models](trained_models) Contains the trained models used in the papers. NOTE: the pytorch version is slightly different from the torch models.\n\n## Requirements:\n\n* [**The Cityscapes dataset**](https://www.cityscapes-dataset.com/): Download the \"leftImg8bit\" for the RGB images and the \"gtFine\" for the labels. **Please note that for training you should use the \"_labelTrainIds\" and not the \"_labelIds\", you can download the [cityscapes scripts](https://github.com/mcordts/cityscapesScripts) and use the [conversor](https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/preparation/createTrainIdLabelImgs.py) to generate trainIds from labelIds**\n* [**Python 3.6**](https://www.python.org/): If you don't have Python3.6 in your system, I recommend installing it with [Anaconda](https://www.anaconda.com/download/#linux)\n* [**PyTorch**](http://pytorch.org/): Make sure to install the Pytorch version for Python 3.6 with CUDA support (code only tested for CUDA 8.0). \n* **Additional Python packages**: numpy, matplotlib, Pillow, torchvision and visdom (optional for --visualize flag)\n\nIn Anaconda you can install with:\n```\nconda install numpy matplotlib torchvision Pillow\nconda install -c conda-forge visdom\n```\n\nIf you use Pip (make sure to have it configured for Python3.6) you can install with: \n\n```\npip install numpy matplotlib torchvision Pillow visdom\n```\n\n## License\n\nThis work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which allows for personal and research use only. For a commercial license please contact the authors. You can view a license summary here: http://creativecommons.org/licenses/by-nc/4.0/\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FEromera%2Ferfnet_pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FEromera%2Ferfnet_pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FEromera%2Ferfnet_pytorch/lists"}