mirror of https://github.com/open-mmlab/mmyolo.git
56 lines
3.1 KiB
Markdown
56 lines
3.1 KiB
Markdown
# Overview
|
|
|
|
This chapter introduces you to the overall framework of MMYOLO and provides links to detailed tutorials.
|
|
|
|
## What is MMYOLO
|
|
|
|
<div align=center>
|
|
<img src="https://user-images.githubusercontent.com/45811724/190993591-bd3f1f11-1c30-4b93-b5f4-05c9ff64ff7f.gif" alt="image">
|
|
</div>
|
|
|
|
MMYOLO is a YOLO series algorithm toolbox, which currently implements only the object detection task and will subsequently support various tasks such as instance segmentation, panoramic segmentation, and key point detection. It includes a rich set of object detection algorithms and related components and modules, and the following is its overall framework.
|
|
|
|
MMYOLO file structure is identical to the MMDetection. To fully reuse the MMDetection code, MMYOLO includes only custom content, consisting of 3 main parts: `datasets`, `models`, `engine`.
|
|
|
|
- **datasets** support a variety of data sets for object detection.
|
|
- **transforms** include various data enhancement transforms.
|
|
- **models** are the most important part of the detector, which includes different components of it.
|
|
- **detectors** define all detection model classes.
|
|
- **data_preprocessors** is used to preprocess the dataset of the model.
|
|
- **backbones** include various backbone networks.
|
|
- **necks** include various neck components.
|
|
- **dense_heads** include various dense heads of different tasks.
|
|
- **losses** include various loss functions.
|
|
- **task_modules** provide components for testing tasks, such as assigners, samplers, box coders, and prior generators.
|
|
- **layers** provide some basic network layers.
|
|
- **engine** is a component of running.
|
|
- **optimizers** provide optimizers and packages for optimizers.
|
|
- **hooks** provide hooks for runner.
|
|
|
|
## How to use this tutorial
|
|
|
|
The detailed instruction of MMYOLO is as follows.
|
|
|
|
1. Look up install instructions to [get_started.md](get_started.md).
|
|
|
|
2. The basic method of how to use MMYOLO can be found here:
|
|
|
|
- [Training and testing](https://mmyolo.readthedocs.io/en/latest/user_guides/index.html#train-test)
|
|
- [From getting started to deployment tutorial](https://mmyolo.readthedocs.io/en/latest/user_guides/index.html#from-getting-started-to-deployment-tutorial)
|
|
- [Useful Tools](https://mmyolo.readthedocs.io/en/latest/user_guides/index.html#useful-tools)
|
|
|
|
3. YOLO series of tutorials on algorithm implementation and full analysis:
|
|
|
|
- [Essential Basics](https://mmyolo.readthedocs.io/en/latest/algorithm_descriptions/index.html#essential-basics)
|
|
- [A full explanation of the model and implementation](https://mmyolo.readthedocs.io/en/latest/algorithm_descriptions/index.html#algorithm-principles-and-implementation)
|
|
|
|
4. YOLO series of Deploy tutorials
|
|
|
|
- [Basic Deployment Guide](https://mmyolo.readthedocs.io/en/latest/deploy/index.html#basic-deployment-guide)
|
|
- [Deployment Tutorial](https://mmyolo.readthedocs.io/en/latest/deploy/index.html#deployment-tutorial)
|
|
|
|
5. Refer to the following tutorials for an in-depth look:
|
|
|
|
- [Data flow](https://mmyolo.readthedocs.io/en/latest/advanced_guides/index.html#data-flow)
|
|
- [How to](https://mmyolo.readthedocs.io/en/latest/advanced_guides/index.html#how-to)
|