Detectron2
Meta AI's PyTorch library for object detection and instance segmentation.
Detectron2 is Meta AI's object detection and segmentation library, written in PyTorch: its model zoo ships Faster R-CNN, Mask R-CNN, RetinaNet, Cascade R-CNN, Panoptic FPN and Keypoint R-CNN pretrained on COCO. A COCO-format dataset registers in the DatasetCatalog, a YAML or LazyConfig configuration drives training, and models export to TorchScript. It's our choice when the job calls for accurate instance masks or panoptic segmentation, rather than the raw speed of a real-time detector.
What Detectron2 brings to your project.
Typical use cases: Industrial quality control, instance segmentation in medical imaging, object counting on aerial photos.
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Model zoo: pretrained Mask R-CNN, Faster R-CNN, RetinaNet, Panoptic FPN.
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Detection, instance segmentation, panoptic and keypoints in one codebase.
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Training on custom COCO datasets, modular backbones and heads, built-in evaluation.
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TorchScript export, Apache 2.0 license, foundation of Mask2Former and Detic.
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A project with Detectron2?
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