# MMDetection — Open-Source Object Detection Toolbox for PyTorch

> MMDetection is an open-source object detection and instance segmentation toolbox from OpenMMLab, offering 300+ pretrained models and a modular config system built on PyTorch.

## Install

Save in your project root:

# MMDetection — Open-Source Object Detection Toolbox for PyTorch

## Quick Use
```bash
pip install openmim
mim install mmengine mmcv mmdet
python -c "from mmdet.apis import init_detector; print('MMDetection installed')"
```

## Introduction
MMDetection is the detection module of the OpenMMLab ecosystem. It provides a unified framework for training and evaluating object detection, instance segmentation, and panoptic segmentation models with a config-driven workflow that makes experimenting with different architectures straightforward.

## What MMDetection Does
- Implements 50+ detection architectures: Faster R-CNN, DETR, YOLOX, Mask R-CNN, and more
- Provides 300+ pretrained model checkpoints on COCO, VOC, and other benchmarks
- Uses a modular config system to mix backbones, necks, heads, and losses without code changes
- Supports distributed training on multiple GPUs and nodes out of the box
- Integrates with MMEngine for logging, visualization, and experiment management

## Architecture Overview
MMDetection follows a registry-and-config pattern. Each component (backbone, neck, head, loss, dataset) is registered by name. A Python config file specifies the full pipeline: `model = dict(type='FasterRCNN', backbone=dict(type='ResNet', depth=50), ...)`. The `Runner` from MMEngine orchestrates training loops, hook execution, and checkpoint management. Data pipelines use composable transforms for augmentation and preprocessing.

## Self-Hosting & Configuration
- Install via MIM: `mim install mmdet` pulls compatible mmcv and mmengine versions
- Config files live in `configs/` organized by model family (e.g., `configs/faster_rcnn/`)
- Override any config field from the command line: `--cfg-options model.backbone.depth=101`
- Use `tools/train.py` for training and `tools/test.py` for evaluation
- Export models to ONNX or TensorRT via `tools/deployment/`

## Key Features
- Broadest collection of detection model implementations in a single codebase
- Config inheritance system reduces boilerplate — change a backbone in one line
- Extensive benchmarks with reproducible results on standard datasets
- Active community with regular releases tracking state-of-the-art methods
- Seamless integration with other OpenMMLab projects (MMSegmentation, MMPose, MMTracking)

## Comparison with Similar Tools
- **Detectron2 (Meta)** — strong alternative with Pythonic config; MMDetection has more model variety
- **Ultralytics (YOLOv8)** — focused on YOLO variants with a simpler API; MMDetection covers far more architectures
- **torchvision.models.detection** — limited set of models shipping with PyTorch
- **PaddleDetection** — PaddlePaddle equivalent; MMDetection is PyTorch-native
- **Hugging Face Transformers** — includes some detection models but not a dedicated detection framework

## FAQ
**Q: Can I use a timm backbone in MMDetection?**
A: Yes. MMDetection supports timm backbones via `mmdet.models.backbones.TIMMBackbone` in the config.

**Q: How do I train on a custom dataset?**
A: Convert your annotations to COCO JSON format, update `data_root` and `ann_file` in the config, and adjust `num_classes` on the head.

**Q: Is MMDetection suitable for production inference?**
A: For production, export the model to ONNX or TensorRT using the deployment tools; the training framework itself is designed for research workflows.

**Q: What is the relationship between MMDetection and MMDet3.x?**
A: MMDetection 3.x is the current major version built on MMEngine, replacing the older MMCV-based runner.

## Sources
- https://github.com/open-mmlab/mmdetection
- https://mmdetection.readthedocs.io/

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Source: https://tokrepo.com/en/workflows/eeb52efe-3e25-11f1-9bc6-00163e2b0d79
Author: AI Open Source