Scaled YOLOv4 is an extension of the YOLOv4 research implemented in the YOLOv5 PyTorch framework.
Here is an overview of the
model:
Scaled YOLOv4 is an extension of the YOLOv4 research, developed by Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao, and implemented in the YOLOv5 PyTorch framework. At its core, it primarily lies on Cross Stage Partial Networks, allowing the network to scale its depth, width, resolution, and structure while maintaining speed and accuracy.
More info here: https://blog.roboflow.com/scaled-yolov4-tops-efficientdet/
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.
Scaled YOLOv4
uses the
uses the
Scaled-YOLOv4 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
Scaled YOLOv4
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
Scaled YOLOv4
to train a computer vision model.
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