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YOLOv4 Darknet

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.

Training on SageMaker

Create a Confusion Matrix

Plot Predictions

Filter Predictions

Blur Predictions

Use SAHI for Small Object Detection

About the model

Here is an overview of the

YOLOv4 Darknet

model:
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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 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.

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 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.

Train YOLOv4-tiny on Custom Data - Lightning Fast Object Detection

Data Augmentation in YOLOv4

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.

A New Video Tutorial: YOLOv4 in PyTorch

YOLOv4 - Ten Tactics to Build a Better Model

The YOLO v4 repository is currently one of the best places to train a custom object detector, and the capabilities of the Darknet repository are vast. In this post, we discuss and implement ten advanced tactics in YOLO v4 so you can build the best object detection model from your custom dataset.

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

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.
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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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