Detectron2 is an open-source Python library for computer vision developed by Facebook AI Research (FAIR). Built on the PyTorch deep learning framework, it provides state-of-the-art algorithms for tasks such as object detection, image segmentation, and keypoint detection.
detectron2.com
detectron2.com
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Key Features and Capabilities
Modular Design: Detectron2 has a flexible, modular architecture that makes it easy for researchers and developers to customize different components like models, layers, and data loading pipelines.
Multiple Computer Vision Tasks: It supports a wide range of computer vision tasks beyond basic object detection, including:
Instance Segmentation.
Panoptic Segmentation.
Keypoint Detection (e.g., human pose estimation).
DensePose (mapping all image pixels to a 3D model of a human).
Semantic Segmentation.
Pre-trained Models (Model Zoo): The library includes a large collection of pre-trained models (called the "Model Zoo") on benchmark datasets like COCO, which can be used for immediate inference or as a starting point for fine-tuning on custom datasets.
Performance: The entire training pipeline has been optimized and moved to GPUs, resulting in faster training speeds compared to its predecessor, the original Caffe2-based Detectron.
Research & Production Ready: It is designed to support both rapid research implementation and production applications, including the ability to export models to formats like TorchScript and ONNX for deployment.
GitHub
GitHub
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Usage
Detectron2 is primarily used via its Python API. Common use cases include:
Running inference on images or video streams using pre-trained models.
Training models on custom datasets by registering data in standard formats like COCO.
Fine-tuning existing models using transfer learning
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