smids_3x_deit_tiny_sgd_00001_fold5
This model is a fine-tuned version of facebook/deit-tiny-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.1096
- Accuracy: 0.3917
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3197 | 1.0 | 225 | 1.3390 | 0.3517 |
| 1.27 | 2.0 | 450 | 1.3176 | 0.3517 |
| 1.3368 | 3.0 | 675 | 1.2980 | 0.36 |
| 1.2821 | 4.0 | 900 | 1.2802 | 0.3583 |
| 1.2856 | 5.0 | 1125 | 1.2641 | 0.3633 |
| 1.2974 | 6.0 | 1350 | 1.2496 | 0.3633 |
| 1.2807 | 7.0 | 1575 | 1.2366 | 0.3733 |
| 1.2428 | 8.0 | 1800 | 1.2247 | 0.3717 |
| 1.2255 | 9.0 | 2025 | 1.2140 | 0.375 |
| 1.2238 | 10.0 | 2250 | 1.2044 | 0.3717 |
| 1.1757 | 11.0 | 2475 | 1.1957 | 0.3733 |
| 1.2719 | 12.0 | 2700 | 1.1877 | 0.38 |
| 1.2029 | 13.0 | 2925 | 1.1807 | 0.38 |
| 1.2184 | 14.0 | 3150 | 1.1743 | 0.3733 |
| 1.2016 | 15.0 | 3375 | 1.1685 | 0.3717 |
| 1.1832 | 16.0 | 3600 | 1.1634 | 0.385 |
| 1.1841 | 17.0 | 3825 | 1.1587 | 0.3883 |
| 1.1122 | 18.0 | 4050 | 1.1544 | 0.395 |
| 1.1798 | 19.0 | 4275 | 1.1504 | 0.3917 |
| 1.1614 | 20.0 | 4500 | 1.1469 | 0.4 |
| 1.1973 | 21.0 | 4725 | 1.1435 | 0.4017 |
| 1.1713 | 22.0 | 4950 | 1.1404 | 0.4083 |
| 1.1334 | 23.0 | 5175 | 1.1376 | 0.4067 |
| 1.1477 | 24.0 | 5400 | 1.1349 | 0.4083 |
| 1.126 | 25.0 | 5625 | 1.1325 | 0.4067 |
| 1.1346 | 26.0 | 5850 | 1.1303 | 0.3983 |
| 1.111 | 27.0 | 6075 | 1.1282 | 0.4 |
| 1.0942 | 28.0 | 6300 | 1.1263 | 0.4067 |
| 1.1712 | 29.0 | 6525 | 1.1245 | 0.405 |
| 1.1249 | 30.0 | 6750 | 1.1229 | 0.4083 |
| 1.13 | 31.0 | 6975 | 1.1213 | 0.4067 |
| 1.1043 | 32.0 | 7200 | 1.1199 | 0.405 |
| 1.1162 | 33.0 | 7425 | 1.1186 | 0.4033 |
| 1.1041 | 34.0 | 7650 | 1.1174 | 0.4017 |
| 1.131 | 35.0 | 7875 | 1.1163 | 0.3983 |
| 1.1255 | 36.0 | 8100 | 1.1153 | 0.3983 |
| 1.0951 | 37.0 | 8325 | 1.1144 | 0.3983 |
| 1.1402 | 38.0 | 8550 | 1.1135 | 0.3967 |
| 1.0931 | 39.0 | 8775 | 1.1128 | 0.3967 |
| 1.1012 | 40.0 | 9000 | 1.1122 | 0.3967 |
| 1.1339 | 41.0 | 9225 | 1.1116 | 0.395 |
| 1.1212 | 42.0 | 9450 | 1.1111 | 0.395 |
| 1.1447 | 43.0 | 9675 | 1.1107 | 0.395 |
| 1.1467 | 44.0 | 9900 | 1.1104 | 0.395 |
| 1.1224 | 45.0 | 10125 | 1.1101 | 0.3917 |
| 1.104 | 46.0 | 10350 | 1.1099 | 0.3917 |
| 1.0801 | 47.0 | 10575 | 1.1098 | 0.39 |
| 1.1097 | 48.0 | 10800 | 1.1097 | 0.3917 |
| 1.0996 | 49.0 | 11025 | 1.1096 | 0.3917 |
| 1.1198 | 50.0 | 11250 | 1.1096 | 0.3917 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2