hushem_1x_deit_tiny_rms_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: 1.1280
- Accuracy: 0.6585
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 |
|---|---|---|---|---|
| No log | 1.0 | 6 | 1.2888 | 0.4390 |
| 1.3565 | 2.0 | 12 | 1.0130 | 0.5366 |
| 1.3565 | 3.0 | 18 | 0.9361 | 0.5366 |
| 0.667 | 4.0 | 24 | 0.8831 | 0.6585 |
| 0.2929 | 5.0 | 30 | 0.8739 | 0.5854 |
| 0.2929 | 6.0 | 36 | 0.9329 | 0.5854 |
| 0.1055 | 7.0 | 42 | 0.9159 | 0.6585 |
| 0.1055 | 8.0 | 48 | 1.0700 | 0.5854 |
| 0.04 | 9.0 | 54 | 1.0357 | 0.5854 |
| 0.013 | 10.0 | 60 | 0.9379 | 0.6585 |
| 0.013 | 11.0 | 66 | 0.9964 | 0.6341 |
| 0.0046 | 12.0 | 72 | 1.0009 | 0.6585 |
| 0.0046 | 13.0 | 78 | 0.9889 | 0.6585 |
| 0.0029 | 14.0 | 84 | 1.0074 | 0.6585 |
| 0.0023 | 15.0 | 90 | 1.0258 | 0.6585 |
| 0.0023 | 16.0 | 96 | 1.0330 | 0.6585 |
| 0.0018 | 17.0 | 102 | 1.0391 | 0.6585 |
| 0.0018 | 18.0 | 108 | 1.0476 | 0.6585 |
| 0.0015 | 19.0 | 114 | 1.0552 | 0.6585 |
| 0.0013 | 20.0 | 120 | 1.0615 | 0.6585 |
| 0.0013 | 21.0 | 126 | 1.0642 | 0.6585 |
| 0.0011 | 22.0 | 132 | 1.0600 | 0.6585 |
| 0.0011 | 23.0 | 138 | 1.0791 | 0.6341 |
| 0.001 | 24.0 | 144 | 1.0890 | 0.6585 |
| 0.001 | 25.0 | 150 | 1.0948 | 0.6585 |
| 0.001 | 26.0 | 156 | 1.1067 | 0.6585 |
| 0.0008 | 27.0 | 162 | 1.0949 | 0.6585 |
| 0.0008 | 28.0 | 168 | 1.1017 | 0.6585 |
| 0.0008 | 29.0 | 174 | 1.1094 | 0.6585 |
| 0.0007 | 30.0 | 180 | 1.1105 | 0.6585 |
| 0.0007 | 31.0 | 186 | 1.1156 | 0.6585 |
| 0.0007 | 32.0 | 192 | 1.1158 | 0.6585 |
| 0.0007 | 33.0 | 198 | 1.1174 | 0.6585 |
| 0.0007 | 34.0 | 204 | 1.1167 | 0.6585 |
| 0.0006 | 35.0 | 210 | 1.1206 | 0.6585 |
| 0.0006 | 36.0 | 216 | 1.1224 | 0.6585 |
| 0.0006 | 37.0 | 222 | 1.1230 | 0.6585 |
| 0.0006 | 38.0 | 228 | 1.1253 | 0.6585 |
| 0.0006 | 39.0 | 234 | 1.1272 | 0.6585 |
| 0.0006 | 40.0 | 240 | 1.1276 | 0.6585 |
| 0.0006 | 41.0 | 246 | 1.1278 | 0.6585 |
| 0.0006 | 42.0 | 252 | 1.1280 | 0.6585 |
| 0.0006 | 43.0 | 258 | 1.1280 | 0.6585 |
| 0.0006 | 44.0 | 264 | 1.1280 | 0.6585 |
| 0.0006 | 45.0 | 270 | 1.1280 | 0.6585 |
| 0.0006 | 46.0 | 276 | 1.1280 | 0.6585 |
| 0.0006 | 47.0 | 282 | 1.1280 | 0.6585 |
| 0.0006 | 48.0 | 288 | 1.1280 | 0.6585 |
| 0.0006 | 49.0 | 294 | 1.1280 | 0.6585 |
| 0.0006 | 50.0 | 300 | 1.1280 | 0.6585 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1