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
model:
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/
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.
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.
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.
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.
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.
Curious about how YOLOv5 compares to other models? Check out our model comparisons.
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