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

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

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

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

In this case, what would be the better option?

This is perfect, I won’t have to invest in additional RAM. Thanks for the tip!

I do the same using numpy files, but they only let me load the whole data which is too big in the first place. Tensorflow let’s us load in batches huh, I’ll look into this

Very interesting, but I wanted the model to preprocess the data only once. This way, it’ll preprocess at every epoch

I haven’t heard of mem mapping, seems like something I should look into, thanks!

I’ve changed the tuner class before. I should try this when I run into this issue

I meant RAM. I know I can reduce the batch size for VRAM. I’ve solved problems by loading the whole dataset into the RAM and training it. But your answer is interesting as well

Exactly what I was thinking. Thanks!

I do hyperparameter tuning too, so the same model will have to train multiple times. More times the better, as I can try more architectures. So speed is important. But you’re saying that 4090 is not much better than 3090 in terms of speed huh

Hi, thanks for your reply. So 2 3090s will be faster than one 4090, correct?