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SeucheAchat9115 t1_iyuzb7c wrote

Reply to comment by somebodyenjoy in [D] Best object detection architecture out there in terms of accuracy alone by somebodyenjoy

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

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somebodyenjoy OP t1_iyv3jls wrote

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

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SeucheAchat9115 t1_iyv5t91 wrote

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

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somebodyenjoy OP t1_iyv5zlq wrote

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

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SeucheAchat9115 t1_iyv631b wrote

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

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somebodyenjoy OP t1_iyv79r7 wrote

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

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SeucheAchat9115 t1_iyv7fcl wrote

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

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somebodyenjoy OP t1_iyv8ila wrote

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

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SeucheAchat9115 t1_iyv8t0s wrote

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

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