A very fast and easy to use PyTorch model that achieves state of the art (or near state of the art) results.
Here is an overview of the
model:
YOLOv5 was released by Glenn Jocher on June 9, 2020. It follows the recent releases of YOLOv4 (April 23, 2020) and EfficientDet (March 18, 2020).
YOLOv5 is smaller and generally easier to use in production. Given it is natively implemented in PyTorch (rather than Darknet), modifying the architecture and exporting to many deploy environments is straightforward.
Read more about YOLOv5 performance.
We've written both a YOLOv5 tutorial and YOLOv5 Colab notebook for training YOLOv5 on your own custom data.
YOLOv5 launched supporting bounding boxes for object detection. Now you can use YOLOv5 for classification and instance segmentation as well.
Using the open-source Autodistill package, you can train efficient models ready for deployment in a few lines of code.
Get startedYOLOv8 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.
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.
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.
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.
YOLOv5
uses the
YOLOv5 PyTorch TXT
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.
Join 100k developers curating high quality datasets and deploying better models with Roboflow.
Get started