What types of models do you know for detection tasks (R-CNN family, YOLO, SSD, DETR)?

There are several popular architectures for object detection tasks in computer vision:

• R-CNN family (R-CNN, Fast R-CNN, Faster R-CNN): two-stage methods where regions of interest are first generated (region proposals), then classified and refined. They provide high accuracy but are slower.

• YOLO (You Only Look Once): a one-stage method that divides the image into a grid and predicts classes and bounding boxes simultaneously. It is fast and suitable for real-time applications.

• SSD (Single Shot MultiBox Detector): also a one-stage detector that uses multiple feature layers to detect objects of different sizes. It balances speed and accuracy.

• DETR (Detection Transformer): a modern approach using transformers for direct object prediction without region proposals. It offers a simple architecture and good results.

Each model has its advantages and is chosen based on requirements for speed and accuracy.