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MMCV
Foundational computer vision library powering the OpenMMLab ecosystem
Apache-2.0
ABOUT
Computer vision research and development requires a vast set of shared utilities — data transforms, CUDA operators, training loops, and file I/O — that each project reimplements independently, leading to fragmentation and duplicated effort. MMCV solves this by providing a unified foundational library with optimized CUDA kernels, standardized data pipelines, a modular training registry, and consistent evaluation tools used across all OpenMMLab projects.
INSTALL
pip install mmcvINTEGRATION GUIDE
1. Access optimized CUDA operators for NMS, RoI pooling, deformable convolution, and other CV primitives without reimplementation
2. Build custom data processing pipelines with configurable transforms for image loading, augmentation, and normalization
3. Leverage the modular training framework with configurable optimizers, schedulers, and evaluation hooks
4. Implement new computer vision models using MMCV's registry system for modular model components and backbones
5. Profile and visualize training runs with built-in logging, checkpoint management, and performance analysis tools
TAGS
computer-visioncuda-operatorsdata-transformstraining-frameworkopenmmlabdeep-learning