YOLO and R-CNN differences

I can’t understand main differences. So, ok, YOLO recognize objects from equal parts of image and faster. But in fact it does the same job, isn’t it? Recognize the type of object and it’s boundaries. And in fact it appears simulaniously. R-CNN maybe recognize by pixel? So, is this main difference?

Hi @someone555777
Its a good question. I will not go into deep details, but both YOLO and R-CNN are OD algorithms. The point is their approach how to detect objects.
YOLO is a single-stage approach that divides the image into a grid and predicts bounding boxes and class probabilities for each grid cell in one pass.
On the other hand, R-CNN is a two-stage approach, where the first stage generates region proposals, and the second stage classifies these proposals into object categories.
This makes YOLO faster than R-CNN at inference time, but it may struggle to localize objects properly compared to R-CNN.
Keep learning!