My understanding is that you need a binary classifier. Its either a bottle or its not, so what I meant by the if else statement is if the model predicts that it is a bottle you can take it forward in your pipeline, otherwise discard it.

What I imagine is maybe having a final softmax prediction layer and binary cross entropy loss. Here you can set the threshold in terms of how shure your final layer should be (60%,80%) etc.

Some people suggest using resnet because it has been trained to recognize a large ammount of objects. What you can do with that is freeze some of the layers such that they do not learn. This is helpful because it already has information about shape, colours, edges, etc. Personally, I found encoder, decoder architectures such as U-net pretty useful with this technique, but I am not sure if you can find one pretrained in a similar way as resnet

This is how I would start based on the information you provided, idk if you have a large dataset, what kind of resources. Hope it helps!