smids_5x_deit_tiny_rms_00001_fold4
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: 1.4112
- Accuracy: 0.8783
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 |
|---|---|---|---|---|
| 0.2498 | 1.0 | 375 | 0.3750 | 0.8583 |
| 0.2126 | 2.0 | 750 | 0.3946 | 0.8617 |
| 0.0815 | 3.0 | 1125 | 0.3928 | 0.8817 |
| 0.1183 | 4.0 | 1500 | 0.4272 | 0.8733 |
| 0.1029 | 5.0 | 1875 | 0.5782 | 0.8833 |
| 0.0245 | 6.0 | 2250 | 0.6426 | 0.8867 |
| 0.0551 | 7.0 | 2625 | 0.8096 | 0.8733 |
| 0.0319 | 8.0 | 3000 | 0.8011 | 0.8733 |
| 0.0533 | 9.0 | 3375 | 0.8429 | 0.875 |
| 0.0056 | 10.0 | 3750 | 0.9672 | 0.8617 |
| 0.0136 | 11.0 | 4125 | 1.0120 | 0.8667 |
| 0.0031 | 12.0 | 4500 | 0.9881 | 0.87 |
| 0.0176 | 13.0 | 4875 | 1.1184 | 0.8767 |
| 0.0127 | 14.0 | 5250 | 1.1325 | 0.8583 |
| 0.0003 | 15.0 | 5625 | 1.2848 | 0.8683 |
| 0.0058 | 16.0 | 6000 | 1.1232 | 0.87 |
| 0.0002 | 17.0 | 6375 | 1.0571 | 0.8817 |
| 0.0421 | 18.0 | 6750 | 1.2079 | 0.8717 |
| 0.0004 | 19.0 | 7125 | 1.2753 | 0.87 |
| 0.0001 | 20.0 | 7500 | 1.3783 | 0.86 |
| 0.0 | 21.0 | 7875 | 1.3177 | 0.865 |
| 0.002 | 22.0 | 8250 | 1.3637 | 0.8633 |
| 0.0002 | 23.0 | 8625 | 1.4459 | 0.87 |
| 0.0005 | 24.0 | 9000 | 1.2813 | 0.875 |
| 0.0 | 25.0 | 9375 | 1.2487 | 0.88 |
| 0.0 | 26.0 | 9750 | 1.2405 | 0.875 |
| 0.0008 | 27.0 | 10125 | 1.3345 | 0.885 |
| 0.0001 | 28.0 | 10500 | 1.5106 | 0.865 |
| 0.0 | 29.0 | 10875 | 1.2765 | 0.8733 |
| 0.0 | 30.0 | 11250 | 1.2626 | 0.875 |
| 0.0332 | 31.0 | 11625 | 1.3653 | 0.8667 |
| 0.0 | 32.0 | 12000 | 1.3469 | 0.8683 |
| 0.0 | 33.0 | 12375 | 1.2524 | 0.8817 |
| 0.0 | 34.0 | 12750 | 1.2947 | 0.8767 |
| 0.0 | 35.0 | 13125 | 1.2962 | 0.8733 |
| 0.0 | 36.0 | 13500 | 1.3559 | 0.8783 |
| 0.0 | 37.0 | 13875 | 1.3878 | 0.8817 |
| 0.0033 | 38.0 | 14250 | 1.3553 | 0.8767 |
| 0.0 | 39.0 | 14625 | 1.4121 | 0.875 |
| 0.0 | 40.0 | 15000 | 1.4174 | 0.875 |
| 0.0 | 41.0 | 15375 | 1.4132 | 0.875 |
| 0.0 | 42.0 | 15750 | 1.4182 | 0.8767 |
| 0.0 | 43.0 | 16125 | 1.4186 | 0.8767 |
| 0.0 | 44.0 | 16500 | 1.4200 | 0.8767 |
| 0.0 | 45.0 | 16875 | 1.4125 | 0.8783 |
| 0.0 | 46.0 | 17250 | 1.4134 | 0.88 |
| 0.0 | 47.0 | 17625 | 1.4114 | 0.8783 |
| 0.0 | 48.0 | 18000 | 1.4108 | 0.8783 |
| 0.0 | 49.0 | 18375 | 1.4113 | 0.8783 |
| 0.0 | 50.0 | 18750 | 1.4112 | 0.8783 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2