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Overview
This chapter introduces you to the overall framework of MMYOLO and provides links to detailed tutorials.
What is MMYOLO
MMYOLO is a YOLO series algorithm toolbox, which currently implements only the target detection task and will subsequently support various tasks such as instance segmentation, panoramic segmentation and key point detection. It includes a rich set of target detection algorithms and related components and modules, and the following is its overall framework.
MMYOLO file structure is identical to the MMDetection. To allow full reuse of the MMDetection code, MMYOLO includes only custom content, which consists of 3 main parts: datasets, models, engine.
- datasets supports a variety of data sets for target detection
- transforms includes various data enhancement transforms
- models is the most important part of the detector, which include different component 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 head of different tasks
- losses include various loss functions
- task_modules provide component 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 following
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Look up install instruction through start your first step.
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Basic method of how to use MMYOLO can be found here:
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YOLO series of tutorials on algorithm implementation and full analysis:
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Refer to the following tutorials for an in-depth look: