Models

What is YOLOv4 PyTorch?

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 PyTorch.

About the model

Here is an overview of the

YOLOv4 PyTorch

model:

Date of Release
Model Type Object Detection
Architecture YOLO
Framework Used PyTorch
Annotation Format YOLOv4 PyTorch TXT
Stars on GitHub 4200+

What is YOLOv4?

YOLOv4 is a real-time object detection model that was published in the April of 2020. It achieved state-of-the-art performance on the COCO dataset for object detection. 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 Procedure

YOLOv4 breaks the object detection task into two pieces, regression to identify object positioning via bounding boxes and classification to determine the object's class. This is similar to the procedure that was used for YOLOv3 (shown below).

YOLOv3 Procedure

Image courtesy of Ethan Yanjia Li

YOLOv4 Results

YOLOv4 performs exceptionally well with both faster speeds and higher mAP than its predecessor, YOLOv3.

YOLOv4 Results

Further Reading

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

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Model Performance

Explore this model on Roboflow

Deploy YOLOv4 PyTorch to production

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YOLOv4 PyTorch Annotation Format

YOLOv4 PyTorch

uses the

uses the

YOLOv4 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.

Convert data between formats

Label data automatically with YOLOv4 PyTorch

You can automatically label a dataset using

YOLOv4 PyTorch

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 PyTorch

to train a computer vision model.

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