Introduction¶
MMCV is a foundational library for computer vision research and supports many research projects as below:
MIM: MIM Installs OpenMMLab Packages.
MMClassification: OpenMMLab image classification toolbox and benchmark.
MMDetection: OpenMMLab detection toolbox and benchmark.
MMDetection3D: OpenMMLab’s next-generation platform for general 3D object detection.
MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
MMAction2: OpenMMLab’s next-generation action understanding toolbox and benchmark.
MMTracking: OpenMMLab video perception toolbox and benchmark.
MMPose: OpenMMLab pose estimation toolbox and benchmark.
MMEditing: OpenMMLab image and video editing toolbox.
MMOCR: A Comprehensive Toolbox for Text Detection, Recognition and Understanding.
MMGeneration: OpenMMLab image and video generative models toolbox.
MMFlow: OpenMMLab optical flow toolbox and benchmark.
MMFewShot: OpenMMLab FewShot Learning Toolbox and Benchmark.
MMHuman3D: OpenMMLab 3D human parametric model toolbox and benchmark.
MMSelfSup: OpenMMLab self-supervised learning Toolbox and Benchmark.
MMRazor: OpenMMLab Model Compression Toolbox and Benchmark.
MMDeploy: OpenMMLab Model Deployment Framework.
It provides the following functionalities.
Universal IO APIs
Image/Video processing
Image and annotation visualization
Useful utilities (progress bar, timer, …)
PyTorch runner with hooking mechanism
Various CNN architectures
High-quality implementation of common CUDA ops
Note
MMCV requires Python 3.6+.