I can train this dataset with resnet18:
python main.py --arch=resnet18 --batch-size=128 dataset
The output after 90 epoch:
Epoch: [89][0/57]	Time 1.645 (1.645)	Data 1.525 (1.525)	Loss 0.3686 (0.3686)	Prec@1 92.969 (92.969)	Prec@5 97.656 (97.656)
Epoch: [89][10/57]	Time 0.364 (0.528)	Data 0.000 (0.244)	Loss 0.5915 (0.5153)	Prec@1 87.500 (87.713)	Prec@5 93.750 (95.455)
Epoch: [89][20/57]	Time 0.510 (0.471)	Data 0.379 (0.190)	Loss 0.6496 (0.5262)	Prec@1 85.938 (87.240)	Prec@5 92.969 (95.126)
Epoch: [89][30/57]	Time 0.355 (0.453)	Data 0.000 (0.171)	Loss 0.4592 (0.5180)	Prec@1 90.625 (87.903)	Prec@5 95.312 (95.186)
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

However, if train this dataset using alexnet with learning rate 0.01:
python main.py --arch=alexnet --lr=0.01 dataset
The output after 90 epoch:
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

You can see with alexnet, after 90 epoch, the precision@1 is 7.458; with resnet18, after 90 epoch, the precision@ is 67.375.
So it is a bit strange to see that the precision of alexnet grows so slowly.