{"id":16431799,"url":"https://github.com/drkostas/3d-semantic-segmentation","last_synced_at":"2025-03-23T08:31:41.134Z","repository":{"id":63639568,"uuid":"490463567","full_name":"drkostas/3D-Semantic-Segmentation","owner":"drkostas","description":"Semantic Segmentation with Transformers on 3D Medical Images","archived":false,"fork":false,"pushed_at":"2022-12-30T18:07:23.000Z","size":44600,"stargazers_count":67,"open_issues_count":0,"forks_count":9,"subscribers_count":9,"default_branch":"main","last_synced_at":"2025-03-15T12:11:28.153Z","etag":null,"topics":["deep-learning","pytorch","segfomer","semantic-segmentation"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/drkostas.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}},"created_at":"2022-05-09T22:31:25.000Z","updated_at":"2025-01-28T17:45:17.000Z","dependencies_parsed_at":"2023-01-31T12:45:52.403Z","dependency_job_id":null,"html_url":"https://github.com/drkostas/3D-Semantic-Segmentation","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/drkostas%2F3D-Semantic-Segmentation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/drkostas%2F3D-Semantic-Segmentation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/drkostas%2F3D-Semantic-Segmentation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/drkostas%2F3D-Semantic-Segmentation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/drkostas","download_url":"https://codeload.github.com/drkostas/3D-Semantic-Segmentation/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245078067,"owners_count":20557274,"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":["deep-learning","pytorch","segfomer","semantic-segmentation"],"created_at":"2024-10-11T08:32:41.411Z","updated_at":"2025-03-23T08:31:39.754Z","avatar_url":"https://github.com/drkostas.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# COSC525: Final Project:  Semantic Segmentation with Transformers on 3D Medical Images\n\n[![GitHub license](https://img.shields.io/badge/license-Apache-blue.svg)](\nhttps://github.com/drkostas/COSC525-Project1/blob/master/LICENSE)\n\n## Table of Contents\n\n+ [About](#about)\n+ [Getting Started](#getting_started)\n    + [Prerequisites](#prerequisites)\n+ [Installing the requirements](#installing)\n+ [Running the code](#run_locally)\n    + [Execution Options](#execution_options)\n        + [main.py](#src_main)\n+ [Todo](#todo)\n+ [License](#license)\n\n## About \u003ca name = \"about\"\u003e\u003c/a\u003e\n\nFinal Project for the Deep Learning course (COSC 525). Involves the development of a semantic \nsegmentation model with transformers on 3D medical images\n\nThe main code is located in the [main.py](main.py) file. All the other code such is located \nin the [src folder](src).\n\n## Getting Started \u003ca name = \"getting_started\"\u003e\u003c/a\u003e\n\nThese instructions will get you a copy of the project up and running on your local machine.\n\n### Prerequisites \u003ca name = \"prerequisites\"\u003e\u003c/a\u003e\n\nYou need to have a machine with Python \u003e 3.6 and any Bash based shell (e.g. zsh) installed.\n\n```ShellSession\n$ python3.9 -V\nPython 3.9.1\n\n$ echo $SHELL\n/usr/bin/zsh\n```\n\n## Installing the requirements \u003ca name = \"installing\"\u003e\u003c/a\u003e\n\nAll the installation steps are being handled by the [Makefile](Makefile). You can either use conda or\nvenv by setting the flag `env=\u003cconda|venv\u003e`. To load an env file use the\nflag `env_file=\u003cpath to env file\u003e`\n\nBefore installing everything, make any changes needed in the [settings.ini](settings.ini) file.\n\nThen, to create a conda environment, install the requirements, setup the library and run the tests\nexecute the following command:\n\n```ShellSession\n$ make install\n```\n\n## Running the code \u003ca name = \"run_locally\"\u003e\u003c/a\u003e\n\nIn order to run the code, you will only need to change the yml file if you need to, and either run its\nfile directly or invoke its console script.\n\n### Execution Options \u003ca name = \"execution_options\"\u003e\u003c/a\u003e\n\nFirst, make sure you are in the correct virtual environment:\n\n```ShellSession\n$ conda activate cosc525_finalproject\n\n$ which python\n/home/\u003cuser\u003e/anaconda3/envs/src/bin/python\n```\n\n#### main.py \u003ca name = \"src_main\"\u003e\u003c/a\u003e\n\nNow, in order to run the code you can call the [main.py](main.py)\ndirectly.\n\n```ShellSession\n$ python main.py -h\nusage: main.py -d DATASET -n NETWORK -c CONFIG_FILE [-l LOG] [-h]\n\nProject 1 for the Deep Learning class (COSC 525). Involves the development of a FeedForward Neural Network.\n\nRequired Arguments:\n  -d DATASET, --dataset DATASET\n                        The datasets to train the network on. Options (defined in yml): [and, xor, class_example]\n  -n NETWORK, --network NETWORK\n                        The network configuration to use. Options (defined in yml): [1x1_net, 2x1_net, 2x2_net]\n  -c CONFIG_FILE, --config-file CONFIG_FILE\n                        The path to the yaml configuration file.\n\nOptional Arguments:\n  -l LOG, --log LOG     Name of the output log file\n  -h, --help            Show this help message and exit\n```\n\n## TODO \u003ca name = \"todo\"\u003e\u003c/a\u003e\n\nRead the [TODO](TODO.md) to see the current task list.\n\n## License \u003ca name = \"license\"\u003e\u003c/a\u003e\n\nThis project is licensed under the Apache License - see the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdrkostas%2F3d-semantic-segmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdrkostas%2F3d-semantic-segmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdrkostas%2F3d-semantic-segmentation/lists"}