I want to build a simple image detector for custom Binary shapes on images. I may train and use the models on object detection zoo such as ssd_inception_v2 and so on. But it's would be extremely un efficient as it has sizes in hundreds of Megabytes. and I can't even imagine to use that in my simple app. can anybody suggest me how to solve this?
I have already built excellent small size classifiers for my images. but can't build small scale efficient detector. (their position with detection boxes)
I think what you need is transfer learning. I would take one of the lightweight models such as MobileNetV2 and retrain on my dataset. It should be pretty quick.If you want to even decrease your model size further, feel free to only take the first few layers of the CNN and retrain it. It would be a bit more work since you need to re-write the part of network you want to use and load it with the pre-trained weights.