Training model on custom data
It has low probability that your model implemented to a camera has no accuracy because you have only 32 images. 
Anyway before you've had about up to 10% loss (It seems to be about 90% accuracy), so it should work  I think the problem is not in the amount of images.
After training your model you need to save the coefficients of model trained. 
Make sure that you implemented model trained, and not you don't use model from scratch
I am trying to try an object detection model on a custom data set. I want it to recognize a specififc piece of metal from my garage. I took like 32 photos and labelled them. The training goes well, but up to 10% loss. After that it goes very slow, so I need to stop it. After that, I implemented the model on camera, but it has no accuracy. Could it be because of the fact that I have only 32 images of the object? I have tried with YoloV2 and Faster RCNN.