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 and annotation visualization
Useful utilities (progress bar, timer, …)
PyTorch runner with hooking mechanism
Various CNN architectures
High-quality implementation of common CUDA ops
MMCV requires Python 3.6+.