MMDetection

// Overview

MMDetection is an open-source object detection toolbox built on PyTorch and is part of the OpenMMLab project. It breaks the detection pipeline into separate modules, so you can mix and match backbones, necks, heads, and training settings to build a custom model without rewriting the whole framework.

It is aimed at computer vision researchers and engineers who need a tested base for detection and segmentation work. Out of the box it covers object detection, instance segmentation, panoptic segmentation, and semi-supervised object detection, and it ships a large model zoo of pre-trained weights you can run or fine-tune.

Within the computer vision space, MMDetection sits alongside frameworks like Detectron2 as a config-driven training and inference library. It relies on two companion OpenMMLab packages, MMEngine for training and MMCV for vision operations, which you install before MMDetection itself.

// What it does

• Modular design that lets you assemble a custom detector by combining backbone, neck, and head components
• Supports object detection, instance segmentation, panoptic segmentation, and semi-supervised object detection out of the box
• Large model zoo of pre-trained configs and weights, including RTMDet and MM-Grounding-DINO
• Core bbox and mask operations run on GPU for fast training and inference
• Config-based workflow for reproducible training and testing on standard datasets like COCO
• Works with PyTorch 1.8+ and integrates with the OpenMMLab MMEngine and MMCV packages

// Getting started

Install PyTorch, then add the OpenMMLab dependencies with mim before installing MMDetection. The example below downloads a small RTMDet model and runs inference on a demo image.

conda install pytorch torchvision -c pytorch

pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0"

mim install mmdet

mim download mmdet --config rtmdet_tiny_8xb32-300e_coco --dest .

from mmdet.apis import init_detector, inference_detector

config_file = 'rtmdet_tiny_8xb32-300e_coco.py'
checkpoint_file = 'rtmdet_tiny_8xb32-300e_coco_20220902_112414-78e30dcc.pth'
model = init_detector(config_file, checkpoint_file, device='cpu')
inference_detector(model, 'demo/demo.jpg')

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Run a pre-trained detector from the model zoo to get bounding boxes or masks on your own images
• Fine-tune an existing detection or instance segmentation model on a custom dataset
• Build and benchmark a new detection architecture by swapping modular components in a config
• Reproduce or compare published detection and segmentation results on standard datasets like COCO

// How MMDetection compares

MMDetection alongside other open-source vision & understanding tools AI/TLDR tracks, ranked by GitHub stars.