I am dealing with the medical images of size 3000x5000 and have around 1500 images. I cant keep default image size to 600 as in the config.py (self.im_size = 600) as it takes long time to train. So I decided to scale down the image size to 300x500. But if I do that, the size of the tumors will be scale down to 32x32 pixel values (very small)... So I want to make sure the receptive fields do not miss the presence of tumors of this size. If the receptive field is large, I think there is a high probability to miss it... please someone could help me to figure out how to change the receptive fields as we want? Is it related to the anchor box sizes in config.py (self.anchor_box_scales = [64, 128, 256]) .
I am dealing with the medical images of size 3000x5000 and have around 1500 images. I cant keep default image size to 600 as in the config.py (self.im_size = 600) as it takes long time to train. So I decided to scale down the image size to 300x500. But if I do that, the size of the tumors will be scale down to 32x32 pixel values (very small)... So I want to make sure the receptive fields do not miss the presence of tumors of this size. If the receptive field is large, I think there is a high probability to miss it... please someone could help me to figure out how to change the receptive fields as we want? Is it related to the anchor box sizes in config.py (self.anchor_box_scales = [64, 128, 256]) .