Model is recognizing background better than objects
You can try to train with masks or do random croppings of the images as a data augmentation strategy or you can change your model for more finer convolutions. The easiest would be to do the random cropping and then, training; that will help the network to disentangle the object from the background.
I created siamese model with triplet loss function.
I tested it a little bit and notice that when object are small, like 2/5 of the image space, model is matching images with similar background instead of object.
Some of pictures were taken on the same background what is causing the issue as I think.
Is there any way to maybe extract objects? train model to recognize those object, ignore background?
Shape of each image is (150, 150, 3).