What is YOLOv3 Keras?

Though it is no longer the most accurate object detection algorithm, YOLO v3 is still a very good choice when you need real-time detection while maintaining excellent accuracy. Keras implementation.

How to Train

YOLOv3 Keras

Training Notebook

Training on SageMaker

About the model

Here is an overview of the

YOLOv3 Keras

model:
‍

Date of Release
Model Type	Object Detection
Architecture
Framework Used	Keras
Annotation Format	YOLO Keras TXT
Stars on GitHub	7100+

What is YOLOv3?

YOLOv3 is an open-source state-of-the-art image detection model. You will find it useful to detect your custom objects. Roboflow provides implementations in both Pytorch and Keras. YOLOv3 has relatively speedy inference times with it taking roughly 30ms per inference. It takes around 270 megabytes to store the approximately 65 million parameter model. There are also variations within YOLOv3 such as Tiny-YOLOv3 which can be used on Rasberry Pi.

YOLOv3 Procedure

YOLOv3 made the initial contribution of framing the object detection problem as a two-step problem to first identify a bounding box (regression problem) and then identify that object's class (classification problem).

Image in Courtesy of Ethan Yanjia Li

YOLOv3 Results

YOLOv3 is an incredibly fast model with it having inference speeds 100-1000x faster than R-CNN. When it was released, YOLOv3 was compared to models like RetinaNet-50 and Retina-Net-101. It had a state-of-the-art performance on the COCO dataset relative to the model's detection speed and inference time, and model size. Below are some of the results comparing YOLOv3 to models of the time.

We find YOLOv3 to have slightly poorer performance than EfficientDet, Scaled YOLOv4, and other modern models on an example 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

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

Training a YOLOv3 Object Detection Model with Custom Dataset

We’ll walk through how to prepare a custom dataset for object detection using tools that simplify image management, architecture, and training.

YOLOv3 Keras Annotation Format

YOLOv3 Keras

uses the

YOLO Keras 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