I guess on Coco the best accuracy is given by transformer networks like Swin, but I would assume your dataset is not as big as coco, therefore transformers might not generalize well.

In this case, what would be the better option?

I guess yolov7 is a good choice, but depends on your institute be aware of the licenses of the code.

EfficientDet if you care about license.

What would be the accuracy of the brute-force approach, i.e. sliding window approach? Would the accuracy be better than all others?

Sliding window approches are „Conventional“ Image Processing techniques which are not comptitive anymore nowadays.

Maybe in terms of speed, but what about accuracy? Wouldn’t it make sense that a classifier going around the image would be more accurate? Is there any research or articles comparing the modern algorithms to sliding windows

Deep Learning Classifiers based on Convolutions also go around the whole image. And the sliding window approaches are not competitive anymore in terms of accuracy as well

I understand, I was asking if we use something like an alexnet and train it on a specific object, like a dog or not detector. Then make this detector go around the entire image in a brute-force manner, would that be more accurate than the object detector models right now

No, because the object detector can solve the problem in a single forward path. Todays deep learning based object detectors like Yolo or RCNN + Swin are very good choices for a detection task

You mean to say they can do better in terms of accuracy even tho they detect in a single forward path?

Yes, because Deep Learning is way better than conventional Methods.

He doesn't know.

if compute doesn't seem to be an issue, why not try what works best on your data?