{"id":18614365,"url":"https://github.com/minar09/u-net-attention","last_synced_at":"2025-04-11T00:30:33.582Z","repository":{"id":67770003,"uuid":"174053679","full_name":"minar09/U-Net-Attention","owner":"minar09","description":"U-Net + Attention, extending U-Net model for semantic segmentation. Implemented with TensorFlow.","archived":false,"fork":false,"pushed_at":"2019-05-11T16:57:40.000Z","size":65,"stargazers_count":11,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-03-25T06:51:12.725Z","etag":null,"topics":["attention","attention-gate","attention-model","attention-to-scale","encoder-decoder","multi-scale","semantic-segmentation","tensorflow","u-net"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/minar09.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-03-06T02:11:31.000Z","updated_at":"2024-05-13T14:50:35.000Z","dependencies_parsed_at":null,"dependency_job_id":"5f4794ed-5430-47cf-909e-4f81557f2b8c","html_url":"https://github.com/minar09/U-Net-Attention","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/minar09%2FU-Net-Attention","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/minar09%2FU-Net-Attention/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/minar09%2FU-Net-Attention/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/minar09%2FU-Net-Attention/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/minar09","download_url":"https://codeload.github.com/minar09/U-Net-Attention/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248322208,"owners_count":21084333,"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":["attention","attention-gate","attention-model","attention-to-scale","encoder-decoder","multi-scale","semantic-segmentation","tensorflow","u-net"],"created_at":"2024-11-07T03:25:55.450Z","updated_at":"2025-04-11T00:30:33.574Z","avatar_url":"https://github.com/minar09.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Fashion parsing models in TensorFlow\n1. Tensorflow implementation of [Fully Convolutional Networks for Semantic Segmentation](http://arxiv.org/pdf/1605.06211v1.pdf) (FCNs).\n2. TensorFlow implementation of [U-Net](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/)\n\nThe implementation is largely based on the reference code provided by the authors of the paper [link](https://github.com/shelhamer/fcn.berkeleyvision.org). \n1. [Prerequisites](#prerequisites)\n2. [Training](#training)\n2. [Testing](#testing)\n2. [Visualizing](#visualizing)\n\n## Prerequisites\n - pydensecrf installation in windows with conda: `conda install -c conda-forge pydensecrf`. For linux, use pip: `pip install pydensecrf`.\n - Check dataset directory in `read_dataset` function of corresponding data reading script, for example, for LIP dataset, check paths in `read_LIP_data.py` and modify as necessary.\n\n## Training\n - To train model simply execute `python FCN.py` or `python UNet.py`\n - You can add training flag as well: `python FCN.py --mode=train`\n - `debug` flag can be set during training to add information regarding activations, gradients, variables etc.\n\n## Testing\n - To test and evaluate results use flag `--mode=test`\n - After testing and evaluation is complete, final results will be printed in the console, and the corresponding files will be saved in the \"logs\" directory.\n \n## Visualizing\n - To visualize results for a random batch of images use flag `--mode=visualize`\n ","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fminar09%2Fu-net-attention","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fminar09%2Fu-net-attention","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fminar09%2Fu-net-attention/lists"}