I am new to machine learning, and have constructed a basic CNN classifier by retraining the last layer of the Inception v3 model with my own image set into two classifications.
I did this in Python using Tensorflow, following the guidelines from here.
I used two files to achieve this:
retrain.py: Retrains the Inception V3 model according to the images in a directory
label_image.py: Labels given images using a prebuilt classifier
My image set contains two folders - one for each object that I am trying to classify. Within these folders are the images. Some images contain very clear views of the object - center-camera, front-facing etc. Others are ordinary photos that contain the object somewhere in view, possibly somewhat obscured or at strange angles.
I imagine that there is some process that I can use to give a higher training 'priority' to the high quality photos of the objects, and a lower priority to the photos where the objects are less obvious or obscured. The reason I want this, is because I have noticed that after including the lower-quality photos, the classifier becomes less confident about its classifications of the high-quality photos, when they should be very easy to classify.
Is what I'm describing a common process? What is it called? I am after keywords or a topic name.