hushem_5x_deit_small_rms_001_fold2
This model is a fine-tuned version of facebook/deit-small-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 3.6556
- Accuracy: 0.3556
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: 0.001
- 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 |
|---|---|---|---|---|
| 2.0324 | 1.0 | 27 | 1.4564 | 0.2444 |
| 1.4819 | 2.0 | 54 | 1.4327 | 0.2444 |
| 1.4504 | 3.0 | 81 | 1.4455 | 0.2667 |
| 1.4703 | 4.0 | 108 | 1.5353 | 0.2444 |
| 1.4319 | 5.0 | 135 | 1.4161 | 0.2444 |
| 1.4127 | 6.0 | 162 | 1.4083 | 0.2444 |
| 1.424 | 7.0 | 189 | 1.4264 | 0.2667 |
| 1.3928 | 8.0 | 216 | 1.4087 | 0.2889 |
| 1.4183 | 9.0 | 243 | 1.3797 | 0.2667 |
| 1.2937 | 10.0 | 270 | 1.5479 | 0.3333 |
| 1.444 | 11.0 | 297 | 1.4212 | 0.2667 |
| 1.2489 | 12.0 | 324 | 1.3827 | 0.3333 |
| 1.2092 | 13.0 | 351 | 1.4109 | 0.3333 |
| 1.1924 | 14.0 | 378 | 1.3647 | 0.3556 |
| 1.1322 | 15.0 | 405 | 1.4486 | 0.4 |
| 1.059 | 16.0 | 432 | 1.3236 | 0.2889 |
| 1.007 | 17.0 | 459 | 1.5059 | 0.3778 |
| 1.0396 | 18.0 | 486 | 1.8214 | 0.3778 |
| 0.9935 | 19.0 | 513 | 1.6035 | 0.2222 |
| 0.9595 | 20.0 | 540 | 1.8699 | 0.3111 |
| 0.9315 | 21.0 | 567 | 1.9455 | 0.2889 |
| 0.9127 | 22.0 | 594 | 1.9720 | 0.1778 |
| 0.9141 | 23.0 | 621 | 1.8863 | 0.4222 |
| 0.8941 | 24.0 | 648 | 2.4630 | 0.2444 |
| 0.861 | 25.0 | 675 | 2.3990 | 0.2 |
| 0.8474 | 26.0 | 702 | 2.1204 | 0.3556 |
| 0.7937 | 27.0 | 729 | 2.7394 | 0.3556 |
| 0.7958 | 28.0 | 756 | 2.5648 | 0.2 |
| 0.7373 | 29.0 | 783 | 2.5253 | 0.3778 |
| 0.7358 | 30.0 | 810 | 2.5059 | 0.3778 |
| 0.691 | 31.0 | 837 | 2.3895 | 0.4222 |
| 0.7103 | 32.0 | 864 | 2.5414 | 0.4222 |
| 0.6539 | 33.0 | 891 | 3.0204 | 0.3333 |
| 0.6275 | 34.0 | 918 | 2.6245 | 0.3778 |
| 0.5921 | 35.0 | 945 | 3.2133 | 0.2667 |
| 0.5912 | 36.0 | 972 | 3.5251 | 0.2667 |
| 0.5547 | 37.0 | 999 | 3.3775 | 0.2889 |
| 0.4976 | 38.0 | 1026 | 3.1294 | 0.4 |
| 0.4303 | 39.0 | 1053 | 3.2846 | 0.3778 |
| 0.3956 | 40.0 | 1080 | 3.2354 | 0.4444 |
| 0.3999 | 41.0 | 1107 | 3.0834 | 0.4667 |
| 0.3745 | 42.0 | 1134 | 3.3561 | 0.3333 |
| 0.3219 | 43.0 | 1161 | 3.3246 | 0.3333 |
| 0.2571 | 44.0 | 1188 | 3.4952 | 0.3556 |
| 0.2544 | 45.0 | 1215 | 3.6528 | 0.3778 |
| 0.2048 | 46.0 | 1242 | 3.6814 | 0.3333 |
| 0.2017 | 47.0 | 1269 | 3.5396 | 0.3778 |
| 0.1409 | 48.0 | 1296 | 3.6629 | 0.3556 |
| 0.1528 | 49.0 | 1323 | 3.6556 | 0.3556 |
| 0.122 | 50.0 | 1350 | 3.6556 | 0.3556 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0