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Created December 6, 2024 18:42
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Roboflow Model Library contains pre-configured model architectures for easily training computer vision models

Roboflow Model Library

The Roboflow Model Library contains pre-configured model architectures for easily training computer vision models.

PyTorch Object Detection

YOLOv8

YOLOv5

YOLOv7

  • Description: The latest in the YOLO mainline, from the creators of YOLOv4, YOLOv7 achieves state of the art performance on MS COCO amongst realtime object detectors.
  • Links:

MT-YOLOv6

YOLOv7 leveraging OpenVINO™ Integration with Torch-ORT

Scaled-YOLOv4

  • Description: As of December 2020, Scaled-YOLOv4 is state-of-the art for object detection. Scaled-YOLOv4 implements YOLOv4 in the PyTorch framework with Cross Stage Partial network layers.
  • Links:

YOLOS

YOLOR

  • Description: You Only Learn One Representation (YOLOR) is a state-of-the-art object detection model that pre-trains an implicit knowledge network and a set of parameters to represent explicit knowledge.
  • Links:

YOLOX

EfficientDet-D0-D7

YOLOv4-tiny

YOLOv4 Darknet

  • Description: YOLOv4 has emerged as the best real time object detection model. YOLOv4 carries forward many of the research contributions of the YOLO family of models along with new modeling and data augmentation techniques. This implementation is in Darknet.
  • Links:

YOLOv5-OBB

Detectron2

  • Description: Detectron2 is a model zoo of its own for computer vision models written in PyTorch. Detectron2 includes all the models that were available in the original Detectron, such as Faster R-CNN, Mask R-CNN, RetinaNet, and DensePose. It also features several new models, including Cascade R-CNN, Panoptic FPN, and TensorMask.
  • Links:

EfficientDet

  • Description: EfficientDet achieves the best performance in the fewest training epochs among object detection model architectures, making it a highly scalable architecture especially when operating with limited compute.
  • Links:

YOLOv4 PyTorch

  • Description: YOLOv4 has emerged as one of the best real-time object detection models. YOLOv4 carries forward many of the research contributions of the YOLO family of models along with new modeling and data augmentation techniques. This implementation is in PyTorch.
  • Links:

Faster R-CNN

YOLO v3 PyTorch

  • Description: Though it is no longer the most accurate object detection algorithm, YOLO v3 is still a very good choice when you need real-time detection while maintaining excellent accuracy. PyTorch version.
  • Links:

YOLO v3 Keras

  • Description: Though it is no longer the most accurate object detection algorithm, YOLO v3 is still a very good choice when you need real-time detection while maintaining excellent accuracy. Keras implementation.
  • Links:

MobileNetSSDv2

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