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.

Scaled YoloV4 Results

Learn More

More info here: https://blog.roboflow.com/scaled-yolov4-tops-efficientdet/

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Check out YOLOv8, defining a new state-of-the-art in computer vision

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.

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Check out YOLOv8, defining a new state-of-the-art in computer vision

Learn about YOLOv8

Model Performance

Explore this model on Roboflow

How to Train Scaled-YOLOv4 to Detect Custom Objects

This blog is written to help you apply Scaled-YOLOv4 to your custom object detection task, to detect any object in the world, given the right training data.

Scaled-YOLOv4 is Now the Best Model for Object Detection

Scaled YOLOv4 Annotation Format

Scaled YOLOv4

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.
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Convert data between formats