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).
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.
Model is recognizing background better than objects