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.
Here is an overview of the
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 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 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.
Training a YOLOv3 Object Detection Model with a Custom Dataset: https://blog.roboflow.com/training-a-yolov3-object-detection-model-with-a-custom-dataset/
Roboflow offers a range of SDKs with which you can deploy your model to production.
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.
You can automatically label a dataset using
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
to train a computer vision model.
Curious about how this model compares to others? Check out our model comparisons.