hushem_1x_deit_small_sgd_0001_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: 1.4641
- Accuracy: 0.2667
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 | 1.5104 | 0.1778 |
| 1.5321 | 2.0 | 12 | 1.5075 | 0.1778 |
| 1.5321 | 3.0 | 18 | 1.5047 | 0.1778 |
| 1.5118 | 4.0 | 24 | 1.5023 | 0.2 |
| 1.5295 | 5.0 | 30 | 1.4998 | 0.2 |
| 1.5295 | 6.0 | 36 | 1.4976 | 0.2 |
| 1.4893 | 7.0 | 42 | 1.4953 | 0.2 |
| 1.4893 | 8.0 | 48 | 1.4932 | 0.2 |
| 1.5068 | 9.0 | 54 | 1.4912 | 0.2 |
| 1.4876 | 10.0 | 60 | 1.4893 | 0.2 |
| 1.4876 | 11.0 | 66 | 1.4876 | 0.2222 |
| 1.4872 | 12.0 | 72 | 1.4858 | 0.2222 |
| 1.4872 | 13.0 | 78 | 1.4842 | 0.2444 |
| 1.482 | 14.0 | 84 | 1.4826 | 0.2444 |
| 1.4925 | 15.0 | 90 | 1.4811 | 0.2444 |
| 1.4925 | 16.0 | 96 | 1.4797 | 0.2444 |
| 1.4692 | 17.0 | 102 | 1.4783 | 0.2444 |
| 1.4692 | 18.0 | 108 | 1.4772 | 0.2444 |
| 1.4971 | 19.0 | 114 | 1.4761 | 0.2444 |
| 1.4368 | 20.0 | 120 | 1.4750 | 0.2444 |
| 1.4368 | 21.0 | 126 | 1.4740 | 0.2444 |
| 1.4645 | 22.0 | 132 | 1.4731 | 0.2444 |
| 1.4645 | 23.0 | 138 | 1.4721 | 0.2444 |
| 1.4558 | 24.0 | 144 | 1.4712 | 0.2667 |
| 1.4397 | 25.0 | 150 | 1.4705 | 0.2667 |
| 1.4397 | 26.0 | 156 | 1.4698 | 0.2667 |
| 1.4566 | 27.0 | 162 | 1.4691 | 0.2667 |
| 1.4566 | 28.0 | 168 | 1.4684 | 0.2667 |
| 1.4686 | 29.0 | 174 | 1.4678 | 0.2667 |
| 1.4549 | 30.0 | 180 | 1.4672 | 0.2667 |
| 1.4549 | 31.0 | 186 | 1.4667 | 0.2667 |
| 1.4527 | 32.0 | 192 | 1.4662 | 0.2667 |
| 1.4527 | 33.0 | 198 | 1.4658 | 0.2667 |
| 1.4549 | 34.0 | 204 | 1.4654 | 0.2667 |
| 1.4704 | 35.0 | 210 | 1.4650 | 0.2667 |
| 1.4704 | 36.0 | 216 | 1.4648 | 0.2667 |
| 1.4264 | 37.0 | 222 | 1.4646 | 0.2667 |
| 1.4264 | 38.0 | 228 | 1.4644 | 0.2667 |
| 1.4286 | 39.0 | 234 | 1.4642 | 0.2667 |
| 1.4743 | 40.0 | 240 | 1.4642 | 0.2667 |
| 1.4743 | 41.0 | 246 | 1.4641 | 0.2667 |
| 1.4713 | 42.0 | 252 | 1.4641 | 0.2667 |
| 1.4713 | 43.0 | 258 | 1.4641 | 0.2667 |
| 1.4345 | 44.0 | 264 | 1.4641 | 0.2667 |
| 1.4282 | 45.0 | 270 | 1.4641 | 0.2667 |
| 1.4282 | 46.0 | 276 | 1.4641 | 0.2667 |
| 1.4413 | 47.0 | 282 | 1.4641 | 0.2667 |
| 1.4413 | 48.0 | 288 | 1.4641 | 0.2667 |
| 1.4233 | 49.0 | 294 | 1.4641 | 0.2667 |
| 1.4542 | 50.0 | 300 | 1.4641 | 0.2667 |
Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1