https://github.com/akaqox/unet-segmentation-with-docker

U-Net segmentation algorithm with options of pretrained resnet34 and resnet50 encoders. All of the project dockerized with gpu suppport on anaconda environment with multiple loss support..
https://github.com/akaqox/unet-segmentation-with-docker

docker docker-container docker-image dockerfile image-processing image-segmentation leaf-disease-classification multiple-loss-function preprocessing python pytorch pytorch-cnn resnet resnet34 resnet50 unet unet-image-segmentation unet-pytorch

Last synced: 3 months ago
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U-Net segmentation algorithm with options of pretrained resnet34 and resnet50 encoders. All of the project dockerized with gpu suppport on anaconda environment with multiple loss support..

• Host: GitHub
• URL: https://github.com/akaqox/unet-segmentation-with-docker
• Owner: Akaqox
• Created: 2024-09-12T06:14:54.000Z (10 months ago)
• Default Branch: master
• Last Pushed: 2024-09-12T12:27:04.000Z (10 months ago)
• Last Synced: 2025-02-11T16:41:41.460Z (5 months ago)
• Topics: docker, docker-container, docker-image, dockerfile, image-processing, image-segmentation, leaf-disease-classification, multiple-loss-function, preprocessing, python, pytorch, pytorch-cnn, resnet, resnet34, resnet50, unet, unet-image-segmentation, unet-pytorch
• Language: Python
• Homepage:
• Size: 19.5 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        



    
 Leaf Segmentation Project with Dockerized Anaconda Environment

A segmentation model has been developed with ability to use multiple loss function options and customizable arguments. The model supports several configurations, including a flat U-Net, as well as U-Net variants with ResNet-34 and ResNet-50 encoders.


To 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.

  

![Python](https://badgen.net/badge/Python/[3.11]/blue?) 

![Pytorch](https://badgen.net/badge/Pytorch/[2.4.0]/red?) 



---

## 💾 **ABOUT**

Will be added later




## Project Structure

Will be added later

  

## 💻 **TECHNOLOGIES**

![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)![OpenCV](https://img.shields.io/badge/opencv-%23white.svg?style=for-the-badge&logo=opencv&logoColor=white)![NumPy](https://img.shields.io/badge/numpy-%23013243.svg?style=for-the-badge&logo=numpy&logoColor=white)![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white)![scikit-learn](https://img.shields.io/badge/scikit--learn-%23F7931E.svg?style=for-the-badge&logo=scikit-learn&logoColor=white)![Anaconda](https://img.shields.io/badge/Anaconda-%2344A833.svg?style=for-the-badge&logo=anaconda&logoColor=white)![Linux](https://img.shields.io/badge/Linux-FCC624?style=for-the-badge&logo=linux&logoColor=black)![Docker](https://img.shields.io/badge/Docker-2CA5E0?style=for-the-badge&logo=docker&logoColor=white)

##  **INSTALLATION**

```

git clone https://github.com/Akaqox/unet-segmentation-with-docker.git

cd unet-segmentation-with-docker

docker build -t seg:latest .

docker run -v /opt/data/seg:/app/results --gpus all -it --ipc=host  seg

python -u main.py --bs --model unet50

python -u inference 

python -u inference --image 'path to image'

python -u inference --jv

```

## 🔎 **SHOWCASE**

 


 Will be added 






 


 





## 🔎 **REFERENCES**












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