YOLOR or YOLOv5 (which one is better)?

YOLO (R or v5) both are object detection algorithms, Before going towards comparison, let's discuss what object detection is.

Object-detection technology is widely used as the backend of many applications in the industry including desktop and web applications. Also, it’s a backbone for many computer vision tasks, which include object segmentation, object tracking, object classification, object counting, etc. In the modern era, The goal of everyone regarding any application is,

The application must be easy to use, take less processing time, and provide the best results.

In the previous few years, many new object detection models came, and every one has its advantages and disadvantages, but until now the best object detection models in terms of speed & accuracy include YOLOv4, YOLOv5, YOLOv7 & YOLO-R.

Fig 1: YOLOv5 vs YOLOR Accuracy Comparison [1]

YOLOv5:

YOLOv5 is a modern object detection algorithm, that has been written in a PyTorch, Besides this, it has, fast speed, high accuracy, easy to install and use.

The importance of YOLOv5 was raised, due to its different export and deployment modules. We can convert the trained model (.pt) into many extensions i.e.,

• torch(.pt) → TensorFlow-lite(.tflite) {for android development, etc)
• torch → open-neural…