I’m working on a project that involves detecting multiple objects in complex images, and traditional methods aren’t delivering the accuracy I need. I’ve read that transformers can improve performance in object detection tasks, but I’m not sure how to implement them effectively. Can someone explain how end-to-end object detection with transformers works, and what the key advantages are over other approaches?
So, transformers are like the cool new kids on the block for object detection. Imagine them as having super vision—they can spot and understand objects in complex images way better than old-school methods. They work end-to-end, meaning they take an image, process it all at once, and directly give you the objects and their locations.
Transformers in object detection, like in DETR (DEtection TRansformers), use an end-to-end approach where they directly predict object bounding boxes and labels without relying on traditional anchor-based methods. They excel in handling complex scenes by leveraging self-attention mechanisms, improving accuracy in detecting multiple objects, and simplifying the overall architecture.