Open Source Computer Vision Object Detection Models

The Roboflow Model Library contains pre-configured model architectures for easily training computer vision models. Just add the link from your Roboflow dataset and you're ready to go! We even include the code to export to common inference formats like TFLite, ONNX, and CoreML.

If you'd like to request a model we haven't yet implemented, please get in touch.

PyTorch Object Detection

YOLOv5

A very fast and easy to use PyTorch model that achieves state of the are (or near state of the art) results. Read More...

Darknet Object Detection

YOLOv4-tiny Darknet

The tiny and fast version of YOLOv4 - good for training and deployment on limited compute resources, and getting a feel for your dataset Read More...

Object Detection

YOLOv4 Darknet

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. Read More...

PyTorch Object Detection

Detectron2

Detectron2 is model zoo of it's 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. Read More...

PyTorch Object Detection

EfficientDet

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. Read More...

Tensorflow 1.5 Object Detection

Faster R-CNN

One of the most accurate object detection algorithms but requires a lot of power at inference time. A good choice if you can do processing asynchronously on a server. Read More...

PyTorch Object Detection

YOLO v3 PyTorch

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. Read More...

Keras Object Detection

YOLO v3 Keras

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. Read More...

Tensorflow 1.5 Object Detection

MobileNetSSDv2

This architecture provides good realtime results on limited compute. It's designed to run in realtime (30 frames per second) even on mobile devices. Read More...

PyTorch Object Detection

YOLOv4 PyTorch

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 PyTorch. Read More...