# Running 10 epochs on the Food-101 dataset

I’m currently working on the Food-101 dataset. 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:

epoch   train_loss  valid_loss  error_rate  time
0   2.526382    1.858536    0.465891    25:21
1   1.981913    1.566125    0.406881    27:21
2   1.748959    1.419548    0.372129    27:16
3   1.611638    1.315319    0.346980    25:16
4   1.568304    1.250232    0.328069    24:43
5   1.438499    1.193816    0.313762    24:26
6   1.378019    1.156924    0.307426    24:30
7   1.331075    1.131671    0.299010    24:26
8   1.314978    1.115857    0.297079    24:24


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?