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imagemed-classifier

ImageMed YOLO-NAS is a tool specialized in medical image classification and analysis. Using advanced YOLO and NAS algorithms

https://github.com/cristiandaksha/imagemed-classifier

Science Score: 44.0%

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Repository

ImageMed YOLO-NAS is a tool specialized in medical image classification and analysis. Using advanced YOLO and NAS algorithms

Basic Info
  • Host: GitHub
  • Owner: cristianDaksha
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 51.8 KB
Statistics
  • Stars: 2
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme License Citation Codeowners

README.md

ImageMed Classifier YOLO-NAS

Description

ImageMed YOLO-NAS is a tool specialized in medical image classification and analysis. Using advanced YOLO and NAS algorithms

Characteristics

  • Advanced Detection and Classification: Use YOLO to identify key features in medical images.
  • Optimization with NAS: Implement NAS techniques to optimize the architecture of neural networks.
  • Focus on Medical Imaging: Specially designed for mammography, cervical imaging and others.

Project Structure

Below is the project directory structure: proyecto_yolo/ │ ├── checkpoints/ # Checkpoints are saved │ | ├── config/ | ├── data/ │ ├── processed/ │ ├── raw/ │ ├── train/ │ ├── test/ │ └── valid/ | ├── logs/ │ └── tarining_logs/ # Logs are saved during training │ ├── models/ # the specific version of YOLO you are using │ └── notebooks/ └── yolo_nas_classifier/ ├── 0.1_data_processing.ipynb # Notebook for data processing ├── 0.2_Training.ipynb # Notebook for the data training process ├── 0.3_evaluation.ipynb # Notebook for model evaluation and data inference └── full_yolo_nas_class.ipynb # Notebook containing all the sessions to develop a neural network

Install

cookiecutter gh:cristianDaksha/ImageMed-Classifier-YOLO-NAS

to install cookiecutter 1. Conda

We create channel with conda conda config --add channels conda-forge

Once the conda-forge channel has been enabled, cookiecutter can be installed with: conda install cookiecutter

  1. Pip

pip install cookiecutter

to install supergradients pip install super-gradients==3.5.0 to install pytorch with cuda pip install torch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 --index-url https://download.pytorch.org/whl/cu118

Owner

  • Login: cristianDaksha
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
title: ImageMed Classifier
message: >-
  If you use this template, please cite it using the
  metadata from this file
type: Software
authors:
- family-names: Cristian
  given-names: Rubio
  email: cristian.rubio@comunidad.unam.mx
  Linkedin: 'https://www.linkedin.com/in/cristian-rubio-ai-dev/'
identifiers:
repository-code: 'https://github.com/cristianDaksha/ImageMed-Classifier.git'
abstract: >-
  ImageMed Classifier, easy to customize
license: MIT

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Dependencies

environment.yml pypi
{{ cookiecutter.project_slug }}/environment.yml pypi
  • matplotlib ==3.8.2
  • numpy ==1.23.0
  • pillow ==10.2.0
  • scikit-learn ==1.4.0
  • seaborn ==0.13.1
  • tensorboard ==2.15.1