Hi @jeremy and everyone!
I was working on an assignment and I published the same as a Kaggle Kernel. It’s a multiclass food classification problem using FOOD-101 dataset which has 101 classes of food. Since it takes too long to train on all 101 classes, I trained the model on 3 classes first and then trained on 11 classes separately to validate and confirm that the model does well even with more number of classes.
The first model trained on 3 classes can classify apple_pie / pizza / omelette. As I was experimenting with different inputs, I gave the model an image which has both applie pie and pizza.
What I noticed was, when the objects in image are flipped vertically, the model changed its output classification from one class to another.
I tried the same with horizontal flip. In this case, the model gave the same output and also with almost similar confidence(92% vs 90%)
I am yet to try this with more images and from different classes. I am curious to know what the reason could be in both cases where the model flipped its output for vertical flip and gave out the same class for a horizontal flip.
Here’s the kernel - https://www.kaggle.com/theimgclist/multiclass-food-classification-using-tensorflow
Note - I used TensorFlow and Keras for this assignment.
Thanks,