> In some of my earlier examples, the model correctly predicts the make/model of cars that are only partially visible in the photo. To study how far this can be

> In some of my earlier examples, the model correctly predicts the make/model of cars that are only partially visible in the photo. To study how far this can be pushed, I panned a crop along a side-view of two different cars to see how the model’s predictions changed as different parts of the car became visible. In these two examples, the model was most accurate when viewing the front or back of the car, but not when only the middle of the car was visible. Perhaps this is a shortcoming of the model, or perhaps automakers customize the front and back shapes of their car more than the sides. I’d be happy to hear other hypotheses as well! I would hypothesize this is an artifact of the training data being photos taken for the purpose of selling the cars. As a seller, it doesn’t make a lot of sense to take a photo of the middle of the car, unless a) you’re trying to hide something or b) you’re being lazy (implying the car isn’t worth much of your time). I would imagine that better photography in general - composition, framing, lighting, setting - would be correlated with higher car prices.