  Iteration of training and evaluating a machine learning model is an important
process to improve its performance. However, while teachable interfaces enable
blind users to train and test an object recognizer with photos taken in their
distinctive environment, accessibility of training iteration and evaluation
steps has received little attention. Iteration assumes visual inspection of the
training photos, which is inaccessible for blind users. We explore this
challenge through MyCam, a mobile app that incorporates automatically estimated
descriptors for non-visual access to the photos in the users' training sets. We
explore how blind participants (N=12) interact with MyCam and the descriptors
through an evaluation study in their homes. We demonstrate that the real-time
photo-level descriptors enabled blind users to reduce photos with cropped
objects, and that participants could add more variations by iterating through
and accessing the quality of their training sets. Also, Participants found the
app simple to use indicating that they could effectively train it and that the
descriptors were useful. However, subjective responses were not reflected in
the performance of their models, partially due to little variation in training
and cluttered backgrounds.
