Hi.
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:
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?