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.

Improvements from Detectron

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.

Check out YOLOv8, defining a new state-of-the-art in computer vision

YOLOv8 is here, setting a new standard for performance in object detection and image segmentation tasks. Roboflow has developed a library of resources to help you get started with YOLOv8, covering guides on how to train YOLOv8, how the model stacks up against v5 and v7, and more.

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Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Model Performance

Explore this model on Roboflow

How to Use the Detectron2 Model Zoo (for Object Detection)

Webinar: How to Build a Detectron2 Model with Roboflow and Paperspace Gradient

How to Train Detectron2 on Custom Object Detection Data

Walk through how to train Detectron2 to detect custom objects in a Detectron2 Colab notebook.

How to Train Detectron2 for Custom Instance Segmentation

How to train Detectron2 to segment your custom objects from any image by providing our model with example training data.

Detectron2 Annotation Format

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.
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Convert data between formats