I’m a Computer Science student currently in my 7th semester, with a strong proficiency in Python. I’m interested in diving into the world of deep learning, specifically focusing on object detection using YOLOv10. However, I’m still new to many of the tools and frameworks commonly used in this field, such as TensorFlow, OpenCV, and PyTorch.

Given that YOLOv10 is the latest version in the YOLO series and introduces several advanced features like NMS-free training and improved efficiency, I’m a bit overwhelmed about where to start. I’m looking for a comprehensive roadmap that can guide me through the prerequisites, necessary skills, and learning resources I need to effectively work with YOLOv10.

Some specific areas I’d like advice on include:

Key foundational topics in deep learning that I should master first.
Resources or tutorials for learning PyTorch and OpenCV, considering I’m new to them.
How to approach understanding object detection concepts, particularly in the context of YOLOv10.
Best practices for setting up a development environment for training and deploying YOLO models.
Any recommended projects or exercises that could solidify my understanding.
I’m eager to hear from anyone who has experience in this field or who has gone through the learning process themselves. Any suggestions or resources that could help me build a strong foundation and advance to working with YOLOv10 would be incredibly valuable.

Thanks in advance for your help!