MMYOLO is an open-source algorithms toolkit of YOLO based on PyTorch and MMDetection, part of the OpenMMLab project. MMYOLO is positioned as a popular open-source library of YOLO series and core library of industrial applications. Its vision diagram is shown as follows:
The following tasks are currently supported:
Tasks currently supported
Rotated object detection
The YOLO series of algorithms currently supported are as follows:
Algorithms currently supported
The datasets currently supported are as follows:
Datasets currently supported
DOTA 1.0 Dataset
MMYOLO runs on Linux, Windows, macOS, and supports PyTorch 1.7 or later. It has the following three characteristics:
🕹️ Unified and convenient algorithm evaluation
MMYOLO unifies various YOLO algorithm modules and provides a unified evaluation process, so that users can compare and analyze fairly and conveniently.
📚 Extensive documentation for started and advanced
MMYOLO provides a series of documents, including getting started, deployment, advanced practice and algorithm analysis, which is convenient for different users to get started and expand.
🧩 Modular Design
MMYOLO disentangled the framework into modular components, and users can easily build custom models by combining different modules and training and testing strategies.
User guide for this documentation¶
MMYOLO divides the document structure into 6 parts, corresponding to different user needs.
Get started with MMYOLO. This part is must read for first-time MMYOLO users, so please read it carefully.
Recommend Topics. This part is the essence documentation provided in MMYOLO by topics, including lots of MMYOLO features, etc. Highly recommended reading for all MMYOLO users.
Common functions. This part provides a list of common features that you will use during the training and testing process, so you can refer back to them when you need.
Useful tools. This part is useful tools summary under
tools, so that you can quickly and happily use the various scripts provided in MMYOLO.
Basic and advanced tutorials. This part introduces some basic concepts and advanced tutorials in MMYOLO. It is suitable for users who want to understand the design idea and structure design of MMYOLO in detail.
Others. The rest includes model repositories, specifications and interface documentation, etc.
Users with different needs can choose your favorite content to read. If you have any questions about this documentation or a better idea to improve it, welcome to post a Pull Request to MMYOLO ~. Please refer to How to Contribute to MMYOLO