Detectron2 is model zoo of it's own for computer vision models written in PyTorch.
Here is an overview of the
model:
Detectron2 is a computer vision model zoo of its own written in PyTorch by the FAIR Facebook AI Research group. Detectron2 includes all the models that were available in the original Detectron, such as Faster R-CNN, Mask R-CNN, RetinaNet, and DensePose as well as some newer models including Cascade R-CNN, Panoptic FPN, and TensorMask. You can use Detectron2 to do key point detection, object detection, and semantic segmentation. Detectron2 registers datasets in COCO JSON format.
Machine Learning Framework: The original detection was written in Caffe2 whereas Detectron2 has made a switch to PyTorch. This allows for developers to take a far more intuitive approach to test and change the model.
Modular Design: Detectron2's modular design allows for the maximum customizability possible; developers can write custom implementations for any part of the object detection model, allowing for greater flexibility within projects.
New Models: Detectron2 includes new models such as Cascade R-CNN, Panoptic FPN, and TensorMask.
Bringing Models to Production: Detectron2 now contains an extra layer called Detectron2go. This makes it easier to bring state-of-the-art models to production through features including model conversions for the cloud and mobile devices, network quantization, and standard training workflows.
Speed: Detectron2's entire training pipeline has been moved to GPUs, making Detectron2 much faster.
Training Detectron2: https://blog.roboflow.com/how-to-train-detectron2/
Using the open-source Autodistill package, you can train efficient models ready for deployment in a few lines of code.
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Detectron2
uses the
COCO JSON
annotation format. If your annotation is in a different format, you can use Roboflow's annotation conversion tools to get your data into the right format.
Curious about how YOLOv5 compares to other models? Check out our model comparisons.
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