I’m currently working on the Food-101 dataset. The problem is similar to what we’ve been working on so far so basically I’m using the exact same approach. I want to train a model that is greater than 85% accuracy for top-1 for the test set, using a ResNet50 or smaller network with a reasonable set of augmentations. I’m running 10 epochs using ResNet34 and I’m currently on the 8th epoch. This is how its doing:
As you can see, it doesn’t seem like I’m going to do better than 71% accuracy at this point. The dataset size is 101,000. It has 101 different kinds of food and each food has a 1000 images. Training this definitely takes long but what are some things I can do to improve its accuracy?