Is there an optimal size on which to run the object detection networks available in the object detection API? The API seems to accept images of all sizes, but it is unclear to me what type and how the image is being rescaled before feeding to the network. Could you please clarify?
Thanks!
There is a script called preprocessor_builder which is responsible for that. So whenever you feed an image to the network it has to go through this preprocessing and makes sure that the image is resized properly to match the network depending on your network configuration file.
And actual resizing is happening here.
The answer is dependent on which model you're running. For our SSD models, we will reshape the image to 300x300 pixels. For FasterRCNN or RFCN, we'll reshape between 600-1024 pixels.
The images the user should add into the TFRecord can be any size, but we recommend users keep sizes as small as possible (ie. ~400-600px max per dimension for SSD, or ~1500px max per dimension for FasterRCNN or R-FCN) for memory reasons.
