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/
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Roboflow offers a range of SDKs with which you can deploy your model to production.
Detectron2
uses the
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.
You can automatically label a dataset using
Detectron2
with help from Autodistill, an open source package for training computer vision models. You can label a folder of images automatically with only a few lines of code. Below, see our tutorials that demonstrate how to use
Detectron2
to train a computer vision model.
Curious about how this model compares to others? Check out our model comparisons.
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