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.
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.
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.
Roboflow offers a range of SDKs with which you can deploy your model to production.
YOLOv5
uses the
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.
You can automatically label a dataset using
YOLOv5
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
YOLOv5
to train a computer vision model.
Curious about how this model compares to others? Check out our model comparisons.
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