Project Introduction: MMDetection
MMDetection is an open-source toolbox for object detection, built on the PyTorch framework. As part of the larger OpenMMLab project, it provides sophisticated tools for various computer vision tasks. The main branch of MMDetection is compatible with PyTorch 1.8 or later versions.
Key Features
Modular Design
The MMDetection framework adopts a modular design approach, decomposing the detection system into numerous components. This breakdown allows users to easily construct custom object detection systems by freely combining different modules according to their needs.
Support for Multiple Tasks
MMDetection caters to a wide array of detection tasks straight out of the box. It supports standard object detection, instance segmentation, panoptic segmentation, and semi-supervised object detection, making it versatile and highly adaptable to various project needs.
High Efficiency
Designed for performance, MMDetection leverages GPU acceleration for basic bounding box (bbox) and mask operations. This ensures the training process is rapid, matching or even surpassing other state-of-the-art frameworks such as Detectron2, maskrcnn-benchmark, and SimpleDet.
State-of-the-Art Performance
Originally developed by the MMDet team, this toolbox has continued to evolve. The team was the winner of the COCO Detection Challenge in 2018. The latest release, RTMDet, offers cutting-edge performance in real-time instance segmentation and rotated object detection, offering a stellar balance between parameter efficiency and accuracy.
Recent Updates
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MM-Grounding-DINO: An extension that provides a comprehensive pipeline for unified object grounding and detection. This initiative explores new data types and evaluates performance across several dimensions, enhancing understanding and application of the Grounding DINO.
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RTMDet: A notable development in real-time object recognition, RTMDet provides an optimized family of single-stage detectors excelling in both parameter-accuracy trade-off and task variety from tiny to extra-large model scales.
Installation
For installation details, please visit Installation to get started with setting up MMDetection on your system.
Getting Started
The Overview section in the documentation is an excellent starting point for newcomers. It provides a clear introduction to MMDetection's capabilities and usage. Detailed instructions are available in the user and advanced guides within the documentation to help users seamlessly navigate through training, testing, and customizing their models.
Model Zoo and Benchmark
MMDetection hosts an extensive model zoo, where users can find a wide range of results and pre-trained models ready for use. An organized showcase of architectures is available, categorized into object detection, instance segmentation, panoptic segmentation, and other specialized models, reflecting the robustness and versatility of MMDetection.
The project also extends support through tools like MMEngine for model training and MMCV for computer vision research, further enriching its ecosystem.
Contribution and Engagement
The MMDetection project welcomes contributions and encourages users to engage through various platforms including Discord and Twitter. Additionally, users can report issues or participate in ongoing projects via the GitHub repository, fostering a collaborative and continuously improving community.
Overall, MMDetection stands as a comprehensive solution for object detection tasks, offering cutting-edge technology wrapped within an efficient and user-friendly framework.