hushem_1x_deit_small_adamax_0001_fold3
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: 0.4128
- Accuracy: 0.8837
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: 0.0001
- 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 | 1.1575 | 0.6047 |
| 1.0751 | 2.0 | 12 | 0.7147 | 0.8140 |
| 1.0751 | 3.0 | 18 | 0.4940 | 0.8372 |
| 0.3179 | 4.0 | 24 | 0.5279 | 0.7674 |
| 0.0721 | 5.0 | 30 | 0.4829 | 0.8140 |
| 0.0721 | 6.0 | 36 | 0.3704 | 0.8837 |
| 0.0092 | 7.0 | 42 | 0.4306 | 0.8605 |
| 0.0092 | 8.0 | 48 | 0.4406 | 0.8605 |
| 0.0018 | 9.0 | 54 | 0.4181 | 0.8605 |
| 0.0009 | 10.0 | 60 | 0.4015 | 0.8837 |
| 0.0009 | 11.0 | 66 | 0.3932 | 0.8837 |
| 0.0006 | 12.0 | 72 | 0.3944 | 0.8837 |
| 0.0006 | 13.0 | 78 | 0.3986 | 0.8837 |
| 0.0005 | 14.0 | 84 | 0.4037 | 0.8837 |
| 0.0004 | 15.0 | 90 | 0.4072 | 0.8837 |
| 0.0004 | 16.0 | 96 | 0.4099 | 0.8837 |
| 0.0004 | 17.0 | 102 | 0.4111 | 0.8837 |
| 0.0004 | 18.0 | 108 | 0.4129 | 0.8837 |
| 0.0003 | 19.0 | 114 | 0.4147 | 0.8837 |
| 0.0003 | 20.0 | 120 | 0.4143 | 0.8837 |
| 0.0003 | 21.0 | 126 | 0.4146 | 0.8837 |
| 0.0003 | 22.0 | 132 | 0.4136 | 0.8837 |
| 0.0003 | 23.0 | 138 | 0.4136 | 0.8837 |
| 0.0003 | 24.0 | 144 | 0.4120 | 0.8837 |
| 0.0003 | 25.0 | 150 | 0.4117 | 0.8837 |
| 0.0003 | 26.0 | 156 | 0.4120 | 0.8837 |
| 0.0003 | 27.0 | 162 | 0.4120 | 0.8837 |
| 0.0003 | 28.0 | 168 | 0.4117 | 0.8837 |
| 0.0003 | 29.0 | 174 | 0.4121 | 0.8837 |
| 0.0003 | 30.0 | 180 | 0.4118 | 0.8837 |
| 0.0003 | 31.0 | 186 | 0.4116 | 0.8837 |
| 0.0002 | 32.0 | 192 | 0.4115 | 0.8837 |
| 0.0002 | 33.0 | 198 | 0.4116 | 0.8837 |
| 0.0002 | 34.0 | 204 | 0.4120 | 0.8837 |
| 0.0002 | 35.0 | 210 | 0.4121 | 0.8837 |
| 0.0002 | 36.0 | 216 | 0.4123 | 0.8837 |
| 0.0002 | 37.0 | 222 | 0.4125 | 0.8837 |
| 0.0002 | 38.0 | 228 | 0.4126 | 0.8837 |
| 0.0002 | 39.0 | 234 | 0.4127 | 0.8837 |
| 0.0002 | 40.0 | 240 | 0.4128 | 0.8837 |
| 0.0002 | 41.0 | 246 | 0.4128 | 0.8837 |
| 0.0002 | 42.0 | 252 | 0.4128 | 0.8837 |
| 0.0002 | 43.0 | 258 | 0.4128 | 0.8837 |
| 0.0002 | 44.0 | 264 | 0.4128 | 0.8837 |
| 0.0002 | 45.0 | 270 | 0.4128 | 0.8837 |
| 0.0002 | 46.0 | 276 | 0.4128 | 0.8837 |
| 0.0002 | 47.0 | 282 | 0.4128 | 0.8837 |
| 0.0002 | 48.0 | 288 | 0.4128 | 0.8837 |
| 0.0002 | 49.0 | 294 | 0.4128 | 0.8837 |
| 0.0002 | 50.0 | 300 | 0.4128 | 0.8837 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1