https://github.com/proafxin/object-detection

Implementation of different object detection algorithms.
https://github.com/proafxin/object-detection

coverage object-detection poetry pytest python pytorch tox yolo

Last synced: 3 months ago
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Implementation of different object detection algorithms.

• Host: GitHub
• URL: https://github.com/proafxin/object-detection
• Owner: proafxin
• License: mit
• Created: 2023-11-01T14:29:29.000Z (over 1 year ago)
• Default Branch: develop
• Last Pushed: 2023-12-16T22:21:38.000Z (over 1 year ago)
• Last Synced: 2025-01-12T02:39:26.233Z (4 months ago)
• Topics: coverage, object-detection, poetry, pytest, python, pytorch, tox, yolo
• Language: Python
• Homepage:
• Size: 610 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Object Detection

Library of different object detection algorithms. You need a CUDA compatible GPU for this to work.

[![Trunk Check CI](https://github.com/proafxin/object-detection/actions/workflows/trunk.yml/badge.svg)](https://github.com/proafxin/object-detection/actions/workflows/trunk.yml)

[![Upload Code Coverage CI](https://github.com/proafxin/object-detection/actions/workflows/ci.yml/badge.svg)](https://github.com/proafxin/object-detection/actions/workflows/ci.yml)

[![codecov](https://codecov.io/gh/proafxin/object-detection/graph/badge.svg?token=ZM9kss9LrH)](https://codecov.io/gh/proafxin/object-detection)

## Development environment

This project uses trunk for linting and formatting. Check 

For dependency, `poetry` is used: 

Go to root directory and run `poetry install`. But first make sure you have CUDA in your system along with all necessary packages. You can follow  for CUDA related instructions. Make sure CUDA can interact with your GPU. The best way to ensure that is to go to python shell and run

```python

import torch

torch.cuda.is_available()

```

You should see `True` as output. Finally, for virtual environment management, automated testing and task management, `tox` is used: 

Install `tox` in your system using `python3 -m pip install -U tox` and then run `tox` from the project directory.

## Design philosophy for YOLO model implementation

The idea behind the structure in this package is that we want to make the model reusable and easily configurable.

We will say, a model consists of some `units`. Each such unit can consist of `layers` or `blocks` or `cells`.

Cell is an individual component such as Convolution, MaxPool, ReLU or other activation function, etc.

A block consists of some cells. Sometimes a single component is repeated multiple times.

To make it easy for us to specify the model, instead of adding the repeated components individually, we simply specify a repeition factor.

The same idea goes for a layer. A layer consists of some blocks which may be repeated.

The idea of `unit` may be confusing at first. First of all, it completely depends on the user. However, a good idea to make a separation would be at where we take maxpool or connected layers. See the following figure:

![Yolov1 architecture](https://i.imgur.com/sIfCQKf.png)

Here, there is a maxpool after the first convolution. So that will be our first unit: convolution combined with maxpool. Same for second unit.

In third unit, we have 4 convolution and then maxpool.

In fourth unit, we have two convolutions which are repeated, then two convolutions and a maxpool layer, and so on.