

YOLOv4 PyTorch

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.

Training on SageMaker

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).

Image courtesy of Ethan Yanjia Li

YOLOv4 Results

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

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

Releasing a New YOLOv3 Implementation in PyTorch

How to Train a YOLOv4 Tiny model and Use TensorFlow Lite

Using the state of the art YOLOv4 tiny Darknet model converted to TensorFlow Lite for on-device inference.

YOLOv4 PyTorch Annotation Format

YOLOv4 PyTorch

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