Project Title

detectron2 — A State-of-the-Art Object Detection and Segmentation Library

Overview

Detectron2 is a next-generation platform developed by Facebook AI Research for object detection, segmentation, and other visual recognition tasks. It is the successor to Detectron and maskrcnn-benchmark, offering new capabilities such as panoptic segmentation, Densepose, and Cascade R-CNN. The library is designed to support both research projects and production applications within Facebook and beyond.

Key Features

Supports a variety of state-of-the-art detection and segmentation algorithms.
Provides new capabilities like panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, DeepLab, ViTDet, MViTv2, etc.
Allows exporting models to TorchScript or Caffe2 formats for deployment.
Offers faster training capabilities.

Use Cases

Researchers and developers in the field of computer vision use Detectron2 to build and deploy object detection and segmentation models.
Production applications within Facebook and other tech companies leverage Detectron2 for visual recognition tasks.
Academic projects utilize Detectron2 for state-of-the-art results in object detection and segmentation.

Advantages

Offers a wide range of cutting-edge algorithms for visual recognition tasks.
Enables easy model deployment with support for TorchScript and Caffe2 formats.
Provides a large set of baseline results and trained models available for download in the Detectron2 Model Zoo.

Limitations / Considerations

The library is primarily focused on object detection and segmentation, which may not cover all computer vision tasks.
As with any advanced research tool, there is a learning curve for new users to effectively utilize Detectron2's full capabilities.

MMDetection: An open-source object detection toolbox based on PyTorch, with a focus on modular design and scalability.
TensorFlow Object Detection API: A flexible framework for building, training, and deploying object detection models.
Darknet: A lightweight, modular neural network framework focused on object detection, known for its speed and efficiency.

Basic Information

GitHub: https://github.com/facebookresearch/detectron2
Stars: 32,803
License: Apache 2.0
Last Commit: 2025-09-16

📊 Project Information

Project Name: detectron2
GitHub URL: https://github.com/facebookresearch/detectron2
Programming Language: Python
⭐ Stars: 32,803
🍴 Forks: 7,791
📅 Created: 2019-09-05
🔄 Last Updated: 2025-09-16

🏷️ Project Topics

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📚 Documentation

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detectron2

Project Description