mmcv
OpenMMLab Computer Vision Foundation
Decision gist · record as of 2026-08-14
Yes, with conditions. MMCV is a mature, widely-used foundation library (6465 GitHub stars, top 15000 PyPI packages) with no known vulnerabilities and permissive licensing. However, install it only if you need its specific utilities—high install friction (source builds are common), aging maintenance (842 days since last release), and no runtime dependencies mean you should verify that its image processing, CNN architectures, or CUDA ops are actually required for your project rather than using PyTorch or OpenCV directly.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- PyTorch must be installed first; for Apple Silicon users, PyTorch 1.13+ is required.
- Pre-built wheels may not exist for all PyTorch/CUDA combinations, requiring source compilation.
- High install friction: the package requires PyTorch to be pre-installed and may need to build from source if a pre-built wheel matching your PyTorch and CUDA versions is unavailable.
License · maintenance · safety
permissive license (permissive) — Licensed under Apache 2.0 (permissive), though the documentation notes that some specific operations within the library carry other licenses; commercial users should review LICENSES.md for details.
last release 2024-04-24 (842 days) · last repo commit 2026-01-29 · 6,465 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 100,349 downloads/mo, #12,989 on PyPI
Alternatives
Verify before relying
pip install -U openmim
mim install mmcv
import mmcv
# Use vision utilities, e.g., mmcv.imread(), mmcv.imwrite()- Whether the aging maintenance status (842 days since release) affects stability or feature completeness for current deep learning workflows.
- Specific performance characteristics of the CUDA ops compared to alternatives or native PyTorch implementations.
- Whether mmcv-lite (the lite variant without CUDA ops) is a viable alternative for projects that do not require GPU acceleration.
What it is and what it does
MMCV is a foundational library maintained by OpenMMLab for computer vision research and applications. It provides core utilities for image and video processing, data transformation pipelines, visualization of images and annotations, standard CNN architectures, and high-performance implementations of common CPU and CUDA operations. The library is designed to be a building block for computer vision projects rather than a complete end-to-end framework.
Version 2.x (the current line) removed training-related components and added a dedicated data transformation module, shifting focus toward being a lightweight vision utility layer. Installation requires PyTorch to be pre-installed and may require building from source if a pre-built wheel is not available for your specific PyTorch and CUDA versions. The package supports Python 3.7+ and runs on Linux, Windows, and macOS.
Use it for
- Build custom computer vision pipelines that need efficient image I/O, resizing, and annotation visualization.
- Integrate optimized CUDA operations for image processing into deep learning training loops.
- Prototype CNN-based models using standard architectures provided by the library.
- Process and transform image datasets with the data transformation module before feeding to models.
- Visualize detection or segmentation results on images and videos during development and evaluation.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
MMCV is a mature, widely-used foundation library (6465 GitHub stars, top 15000 PyPI packages) with no known vulnerabilities and permissive licensing. However, install it only if you need its specific utilities—high install friction (source builds are common), aging maintenance (842 days since last release), and no runtime dependencies mean you should verify that its image processing, CNN architectures, or CUDA ops are actually required for your project rather than using PyTorch or OpenCV directly.
Install
mmcv on PyPI
Before you install
High install friction: the package requires PyTorch to be pre-installed and may need to build from source if a pre-built wheel matching your PyTorch and CUDA versions is unavailable. Maintenance status is aging—last release was 842 days ago, though the repository remains active with recent commits.
PyTorch must be installed first; for Apple Silicon users, PyTorch 1.13+ is required. Pre-built wheels may not exist for all PyTorch/CUDA combinations, requiring source compilation.
License in practice
Licensed under Apache 2.0 (permissive), though the documentation notes that some specific operations within the library carry other licenses; commercial users should review LICENSES.md for details.
Quickstart
pip install -U openmim
mim install mmcv
import mmcv
# Use vision utilities, e.g., mmcv.imread(), mmcv.imwrite()Verify before relying
- Whether the aging maintenance status (842 days since release) affects stability or feature completeness for current deep learning workflows.
- Specific performance characteristics of the CUDA ops compared to alternatives or native PyTorch implementations.
- Whether mmcv-lite (the lite variant without CUDA ops) is a viable alternative for projects that do not require GPU acceleration.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | High. Source build required |
| Runtime dependencies | None |
| Maintenance | Aging 842 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 100,349 / month, #12,989 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Utilities |
Evidence: mmcv-2.2.0.tar.gz
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “computer vision foundation library”
- mmcvMMCV is a foundational library for computer vision that provides…
- inference-cliA command-line tool for running computer vision inference locally via…
- superviselyPython SDK for the Supervisely computer vision platform, providing…
Give your agent the search over MCP, or paste the wish link into any chat.
More Utilities packages
Converts domain names between Unicode and ASCII-compatible encoding (Punycode) according to IDNA 2008 and Unicode Technical Standard 46, with security validation and broader script coverage than the standard library.
Install it if you work with internationalized domain names, need to validate domains, or use HTTP clients that depend on it transitively.
Detects and normalizes text encoding from unknown or ambiguous sources, supporting all IANA character sets that Python's core library provides codecs for, with the ability to register custom codecs.
Setuptools is a Python build backend and package management tool that handles building, distributing, and installing Python packages, including support for C/C++ extension modules.
Pluggy provides a plugin system that lets you define hook specifications and register implementations to be called in sequence, enabling extensible Python applications without tight coupling.
Install it if you're building an extensible application or framework.
Pygments is a syntax highlighter that colorizes source code and text in over 500 languages and formats, outputting to HTML, LaTeX, RTF, SVG, images, or ANSI terminal sequences.
Install it if you need to display or transform source code.
Six provides utility functions to write Python code that runs on both Python 2.7 and Python 3.3+, smoothing over language differences between the two versions.
See also cvat-sdk · mmdet · openmim · mmengine · voxel51-eta · torchvision · pytorchcv · icevision · fiftyone-db · libcuvs-cu12