{"id":15129820,"url":"https://github.com/akaqox/unet-segmentation-with-docker","last_synced_at":"2026-01-20T15:07:33.213Z","repository":{"id":256722394,"uuid":"856182171","full_name":"Akaqox/unet-segmentation-with-docker","owner":"Akaqox","description":"U-Net segmentation algorithm with options of pretrained resnet34  and resnet50 encoders. 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The model supports several configurations, including a flat U-Net, as well as U-Net variants with ResNet-34 and ResNet-50 encoders.\u003c/p\u003e\n\n\u003cp\u003eTo ensure compatibility across different environments, the entire project has been containerized using Docker. This allows for a plug-and-play approach, simplifying the process of running the model in various setups.\u003c/p\u003e\n  \n![Python](https://badgen.net/badge/Python/[3.11]/blue?) \n![Pytorch](https://badgen.net/badge/Pytorch/[2.4.0]/red?) \n\u003c/div\u003e\n\n---\n\n## 💾 **ABOUT**\n\nWill be added later\n\n\u003cbr /\u003e\n\n## Project Structure\n\nWill be added later\n  \n## 💻 **TECHNOLOGIES**\n\n![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge\u0026logo=python\u0026logoColor=ffdd54)![OpenCV](https://img.shields.io/badge/opencv-%23white.svg?style=for-the-badge\u0026logo=opencv\u0026logoColor=white)![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge\u0026logo=numpy\u0026logoColor=white)![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge\u0026logo=PyTorch\u0026logoColor=white)![scikit-learn](https://img.shields.io/badge/scikit--learn-%23F7931E.svg?style=for-the-badge\u0026logo=scikit-learn\u0026logoColor=white)![Anaconda](https://img.shields.io/badge/Anaconda-%2344A833.svg?style=for-the-badge\u0026logo=anaconda\u0026logoColor=white)![Linux](https://img.shields.io/badge/Linux-FCC624?style=for-the-badge\u0026logo=linux\u0026logoColor=black)![Docker](https://img.shields.io/badge/Docker-2CA5E0?style=for-the-badge\u0026logo=docker\u0026logoColor=white)\n\n##  **INSTALLATION**\n\n```\ngit clone https://github.com/Akaqox/unet-segmentation-with-docker.git\ncd unet-segmentation-with-docker\ndocker build -t seg:latest .\ndocker run -v /opt/data/seg:/app/results --gpus all -it --ipc=host  seg\n\npython -u main.py --bs --model unet50\npython -u inference \npython -u inference --image 'path to image'\npython -u inference --jv\n```\n\n## 🔎 **SHOWCASE**\n \u003ch2\u003e\u003cb\u003e Will be added \u003c/b\u003e\u003c/h1\u003e\n\u003cimg src=/\u003e\n\u003cbr /\u003e\n \u003ch2\u003e\u003cb\u003e \u003c/b\u003e\u003c/h1\u003e\n\n\u003cbr /\u003e\n\n## 🔎 **REFERENCES**\n\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\n\n\u003cbr /\u003e\n\n---\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakaqox%2Funet-segmentation-with-docker","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fakaqox%2Funet-segmentation-with-docker","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakaqox%2Funet-segmentation-with-docker/lists"}