MMCV 시작하기

Introduction

※ MMCV는 컴퓨터 비전 연구를 위한 기본 라이브러리(foundational library)이다.

• Data Process
• Visualization
• Data Transformation
• Various CNN Architectures
• High-quality Implementation of Common CUDA Ops

 

▶ MMCV는 다양한 리서치 프로젝트를 지원한다:

• MMClassification: OpenMMLab image classification toolbox and benchmark.
• MMDetection: OpenMMLab detection toolbox and benchmark.
• MMDetection3D: OpenMMLab’s next-generation platform for general 3D object detection.
• MMRotate: OpenMMLab rotated object detection toolbox and benchmark.
• MMYOLO: OpenMMLab YOLO series toolbox and benchmark.
• MMSegmentation: OpenMMLab semantic segmentation toolbox and benchmark.
• MMOCR: OpenMMLab text detection, recognition, and understanding toolbox.
• MMPose: OpenMMLab pose estimation 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.
• MMFewShot: OpenMMLab fewshot learning toolbox and benchmark.
• MMAction2: OpenMMLab’s next-generation action understanding toolbox and benchmark.
• MMTracking: OpenMMLab video perception toolbox and benchmark.
• MMFlow: OpenMMLab optical flow toolbox and benchmark.
• MMEditing: OpenMMLab image and video editing toolbox.
• MMGeneration: OpenMMLab image and video generative models toolbox.
• MMDeploy: OpenMMLab model deployment framework.

 

https://mmcv.readthedocs.io/en/latest/get_started/introduction.html

 

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Installation

1. PyTorch 설치하기

• PyTorch: Deep Learning Framework

Start Locally

 

2. mim 이용하여 MMCV 설치하기

• mim: mim은 OpenMMLab 프로젝트용 패키지 관리 도구로, 이를 이용해 mmcv를 쉽게 설치할 수 있다.

pip install -U openmim
mim install "mmcv>=2.0.1"

 

https://mmcv.readthedocs.io/en/latest/get_started/installation.html