| Sumario: | Learn how you can implement and train your own custom YOLOv4 object detection models in computer vision. This course is a perfect fit if you want to natively train your own YOLOv4 neural network. You'll start off with a gentle introduction to the world of computer vision with YOLOv4, install darknet, and build libraries for YOLOv4 to implement YOLOv4 on images and videos in real-time. You'll even solve current and relevant real-world problems by building your own social distancing monitoring app and implementing vehicle tracking using the robust DeepSORT algorithm. After that, you'll learn more techniques and best practices/rules of how to take your Python implementations and develop GUIs for your YOLOv4 apps using PyQT. Then, you'll be labeling your own dataset from scratch, converting standard datasets into YOLOv4 format, amplifying your dataset 10x, and employing data augmentation to significantly increase the diversity of available data for training models, without collecting new data. Finally, you'll develop your own Mask Detection app to detect whether a person is wearing their mask and to flag an alert. By the end of this course, you'd be able to implement and train your own custom CNNs with YOLOv4. It will help you in solving real-world problems, freelancing AI projects, getting that opportunity in AI, and tackling your research work by saving time and money. The world is your oyster; just start exploring the world once you have skills in AI.
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