Models

What is 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.

About the model

Here is an overview of the

YOLOv4 Darknet

model:

Date of Release
Model Type Object Detection
Architecture
Framework Used Darknet
Annotation Format YOLO Darknet TXT
Stars on GitHub 20100+

What is YOLOv4?

YOLOv4 was a real-time object detection model published in April 2020 that achieved state-of-the-art performance on the COCO dataset. It works by breaking the object detection task into two pieces, regression to identify object positioning via bounding boxes and classification to determine the object's class. This implementation of YoloV4 uses the Darknet framework.

By using YOLOv4, you are implementing many of the past research contributions in the YOLO family along with a series of new contributions unique to YOLOv4 including new features: WRC, CSP, CmBN, SAT, Mish activation, Mosaic data augmentation, CmBN, DropBlock regularization, and CIoU loss. In short, with YOLOv4, you're using a better object detection network architecture and new data augmentation techniques.

YOLOv4 Architecture

YOLOv4 Architecture

YOLOv4 Results

As can be seen in the results below, YOLOv4 has an incredibly high performance for a very high FPS; this was a major improvement from previous object detection models which only had either high performance or high inference speeds.

YOLOv4 Results

Further Reading about YOLOv4

Breaking Down YOLOv4: https://blog.roboflow.com/a-thorough-breakdown-of-yolov4/
Training YOLOv4 on a Custom Dataset: https://blog.roboflow.com/training-yolov4-on-a-custom-dataset/

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

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

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

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

Model Performance

Explore this model on Roboflow

Deploy YOLOv4 Darknet to production

Roboflow offers a range of SDKs with which you can deploy your model to production.

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YOLOv4 Darknet Annotation Format

YOLOv4 Darknet

uses the

uses the

YOLO Darknet 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.

Convert data between formats

Label data automatically with YOLOv4 Darknet

You can automatically label a dataset using

YOLOv4 Darknet

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

YOLOv4 Darknet

to train a computer vision model.

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