Precision doesn’t improve when training alexnet and vgg16 on custom dataset

[panovr]

I use pytorch imagenet example on a custom dataset. My dataset has nearly 300 categories, and 12000 images totally. The dataset is organized in train and val directories.

• alextnet with learning rate 0.1 - python main.py --arch=alexnet dataset

Epoch: [49][0/29]	Time 3.213 (3.213)	Data 3.137 (3.137)	Loss 5.7120 (5.7120)	Prec@1 0.781 (0.781)	Prec@5 1.172 (1.172)
Epoch: [49][10/29]	Time 0.182 (0.869)	Data 0.000 (0.713)	Loss 5.7154 (5.7094)	Prec@1 0.000 (0.426)	Prec@5 0.781 (1.456)
Epoch: [49][20/29]	Time 2.013 (0.829)	Data 1.931 (0.696)	Loss 5.7096 (5.7113)	Prec@1 0.781 (0.316)	Prec@5 2.734 (1.376)
Test: [0/10]	Time 3.072 (3.072)	Loss 5.7060 (5.7060)	Prec@1 0.000 (0.000)	Prec@5 0.000 (0.000)
 * Prec@1 0.333 Prec@5 1.667

• alexnet with learning rate 0.01 - python main.py --arch=alexnet --lr=0.01 dataset

Epoch: [89][0/29]	Time 3.110 (3.110)	Data 3.040 (3.040)	Loss 4.7523 (4.7523)	Prec@1 5.469 (5.469)	Prec@5 19.922 (19.922)
Epoch: [89][10/29]	Time 0.189 (0.831)	Data 0.070 (0.700)	Loss 4.7577 (4.8041)	Prec@1 6.250 (5.611)	Prec@5 19.141 (17.685)
Epoch: [89][20/29]	Time 2.163 (0.831)	Data 2.079 (0.705)	Loss 4.8331 (4.8019)	Prec@1 4.688 (5.673)	Prec@5 19.531 (17.839)
Test: [0/10]	Time 3.048 (3.048)	Loss 4.6815 (4.6815)	Prec@1 8.203 (8.203)	Prec@5 23.047 (23.047)
 * Prec@1 7.458 Prec@5 22.833

• vgg16 with learning rate 0.01 - basically the same as alexnet, the training didn't convergent.

• However, I can train this dataset with resnet18:
  python main.py --arch=resnet18 --batch-size=128 dataset

Epoch: [89][40/57]	Time 0.536 (0.446)	Data 0.413 (0.165)	Loss 0.3770 (0.5072)	Prec@1 92.188 ```
(88.529)	Prec@5 97.656 (95.332)
Epoch: [89][50/57]	Time 0.369 (0.440)	Data 0.000 (0.166)	Loss 0.4453 (0.5025)	Prec@1 89.844 (88.664)	Prec@5 95.312 (95.374)
Test: [0/19]	Time 1.668 (1.668)	Loss 0.8600 (0.8600)	Prec@1 81.250 (81.250)	Prec@5 94.531 (94.531)
Test: [10/19]	Time 0.104 (0.463)	Loss 1.5666 (1.5452)	Prec@1 67.188 (67.827)	Prec@5 84.375 (84.659)
 * Prec@1 67.375 Prec@5 84.208

[soumith]

this is not unexpected. This can be for a variety of reasons, and is not a bug at all.