CVA-NET: COMPUTER VISION ANALYSIS NETWORK

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This repository contents different scripts for different models to allow you to train models of images classification, detection and generation.

Table of Contents

• Description
• Installation
  • For Linux
  • For Windows
• Usage
• Features
• Tests
• To contribute
• Licence
• Contact

Installation

To install the project, make sure you have Python 3.10 or later version and pip installed on your machine. And then run the following command lines.

For Linux

git clone git@github.com:mokira3d48/CVA_NET.git CVA_NET;
cd CVA_NET;
sudo rm -r .git;
git init;  # To create a new instance of git repository

And then,

1. sudo apt install cmake python3-venv Install Cmake and Virtual env;
2. python3 -m venv .venv create a virtual env into directory named env;
3. source .venv/bin/activate activate the virtual environment named .venv;
4. make install install the requirements of this package;
5. pip install -e . install the package in dev mode in virtual environment;
6. make test run the unit test scripts located at tests directory;

For Windows

git clone git@github.com:mokira3d48/CVA_NET.git CVA_NET

cd CVA_NET

And then, delete the hidden directory named .git located at the root of the directory project.

And then,

1. Install python for windows;
2. Open your command prompt;
3. Run python -m venv .venv to create a virtual env into directory named .venv;
4. Run .venv\Scripts\activate to activate the virtual environment;
5. Run pip install -r requirements.txt to install the requirements of this package or project;
6. Run pip install -e . install the package in dev mode in virtual environment. pytest run the unit test scripts located at tests directory;

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Usage

Features

1. AlexNet

• Model Training;
• Model fine-tuning;

2. VAE

• Model Training;
• Fine-tuning VAE encoder with 1-layer and 3-layers of Fully connected.

3. YOLO

• Model Training;

Tests

To execute the unittest, make sure you have pytest package installed, and then run the following command line:

make test 

or

pytest

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To contribute

Contributions are welcome! Please follow these steps:

1. Create a new branch for your feature (git checkout -b feature/my-feature);
2. Commit your changes (git commit -m 'Adding a new feature');
3. Push toward the branch (git push origin feature/my-feature);
4. Create a new Pull Request or Merge Request.

Licence

This project is licensed under the MIT License. See the file LICENSE for more details, contact me please.

Contact

For your question or suggestion, contact me please:

• Name : Doctor Mokira
• Email : dr.mokira@gmail.com
• GitHub : mokira3d48