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This video describes YOLOv5 Object Detector. It is a Real-Time Object Detector and is a PyTorch implementation of YOLO SSD known for its blazingly fast speed and very good Accuracy. YOLO is a Single Stage Detector that processes the input image only once to detect an object. It is different from Two-Stage Detectors like Faster RCNN. YOLOv1-v4 are implemented on DarkNet whereas YOLOv5 is built on top of PyTorch implementation of YOLOv3. It has 5 different Models YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. Each model is different in size and has a different use case. To know more about each model please refer to our you can to this Blogpost: This video explains various different ways to export YOLOv5 that allow you to use it very easily. We have also discussed the Logging and Visualization method that helps us to keep track of its Training process. Time Stamps: 0:00-01:09: Introduction 01:09-3:09: Different Models of YOLOv5 3:09-4:
