What you'll learn Fundamentals of YOLOv7 Training YOLOv7 in Custom Dataset Deploying&Testing the Model in YOLOv7 Data Processing, Segmentation, Data Collection, and Essential Prerequisites in YOLOv7 Requirements Python Programming Basics OpenCV Programming Description 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. The YOLO (You Only Look Once) model is a single-stage object detector. Image frames are featured through a backbone, features are combined and mixed in the neck, and then they are passed along to the head of the network where YOLO predicts the bounding box locations, the classes of the bounding boxes, and the objectness of the bounding boxes. With its amazing characteristics, YOLOv7 is a real-time object detector that is now transforming computer vision.In comparison to its earlier iterations, the official YOLOv7 offers incredible speed and precision. YOLOv7 weights are trained without pre-trained weights using the COCO dataset from Microsoft. With this full-stack object detection course, you will learn to code YOLOv7 from the basics, training your custom YOLOv7, multi-object tracking system, Flask integrations, various segmentation, and object detection feature to its most practical applications. You will also have access to the most delicate details of this state-of-the-art system.In this course, we will share the first two modules of our full-stack course. Module 1 - Getting Started with YOLOv7Module 2 - Training YOLOv7 on Custom Dataset This course is highly recommended to anyone interested in computer vision!
