smids_10x_deit_small_rms_0001_fold5
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: 1.1181
- Accuracy: 0.8983
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 |
|---|---|---|---|---|
| 0.1916 | 1.0 | 750 | 0.2932 | 0.8983 |
| 0.1713 | 2.0 | 1500 | 0.3029 | 0.9083 |
| 0.1109 | 3.0 | 2250 | 0.5067 | 0.8767 |
| 0.0732 | 4.0 | 3000 | 0.4780 | 0.91 |
| 0.102 | 5.0 | 3750 | 0.4476 | 0.8983 |
| 0.0951 | 6.0 | 4500 | 0.5447 | 0.9017 |
| 0.0348 | 7.0 | 5250 | 0.5626 | 0.915 |
| 0.0144 | 8.0 | 6000 | 0.5893 | 0.9133 |
| 0.0592 | 9.0 | 6750 | 0.5568 | 0.91 |
| 0.0154 | 10.0 | 7500 | 0.5698 | 0.9017 |
| 0.0271 | 11.0 | 8250 | 0.6256 | 0.8983 |
| 0.0176 | 12.0 | 9000 | 0.7391 | 0.8917 |
| 0.012 | 13.0 | 9750 | 0.6256 | 0.8967 |
| 0.0094 | 14.0 | 10500 | 0.7473 | 0.895 |
| 0.0301 | 15.0 | 11250 | 0.6066 | 0.905 |
| 0.0069 | 16.0 | 12000 | 0.6970 | 0.9 |
| 0.0131 | 17.0 | 12750 | 0.6902 | 0.895 |
| 0.0022 | 18.0 | 13500 | 0.7962 | 0.8833 |
| 0.0162 | 19.0 | 14250 | 0.8033 | 0.8967 |
| 0.0045 | 20.0 | 15000 | 0.7612 | 0.8933 |
| 0.0063 | 21.0 | 15750 | 0.7939 | 0.9 |
| 0.0002 | 22.0 | 16500 | 0.7612 | 0.8933 |
| 0.0161 | 23.0 | 17250 | 0.8161 | 0.8867 |
| 0.0001 | 24.0 | 18000 | 0.8196 | 0.8933 |
| 0.038 | 25.0 | 18750 | 0.8702 | 0.8883 |
| 0.0257 | 26.0 | 19500 | 0.7862 | 0.8983 |
| 0.0004 | 27.0 | 20250 | 0.8138 | 0.8983 |
| 0.0001 | 28.0 | 21000 | 0.8830 | 0.905 |
| 0.0003 | 29.0 | 21750 | 1.0169 | 0.89 |
| 0.016 | 30.0 | 22500 | 0.8531 | 0.8883 |
| 0.0027 | 31.0 | 23250 | 0.9699 | 0.895 |
| 0.0379 | 32.0 | 24000 | 1.0313 | 0.89 |
| 0.0307 | 33.0 | 24750 | 0.8698 | 0.905 |
| 0.0 | 34.0 | 25500 | 0.8949 | 0.9017 |
| 0.0 | 35.0 | 26250 | 0.9260 | 0.8917 |
| 0.0 | 36.0 | 27000 | 0.9677 | 0.89 |
| 0.0036 | 37.0 | 27750 | 1.0175 | 0.9017 |
| 0.0 | 38.0 | 28500 | 1.0579 | 0.8967 |
| 0.0003 | 39.0 | 29250 | 1.1044 | 0.8917 |
| 0.0 | 40.0 | 30000 | 1.0357 | 0.895 |
| 0.0 | 41.0 | 30750 | 1.0321 | 0.9 |
| 0.0 | 42.0 | 31500 | 1.0802 | 0.895 |
| 0.0 | 43.0 | 32250 | 1.1064 | 0.895 |
| 0.0 | 44.0 | 33000 | 1.1154 | 0.9017 |
| 0.0 | 45.0 | 33750 | 1.1069 | 0.9017 |
| 0.0 | 46.0 | 34500 | 1.1200 | 0.895 |
| 0.0 | 47.0 | 35250 | 1.1205 | 0.8967 |
| 0.0 | 48.0 | 36000 | 1.1169 | 0.8983 |
| 0.0 | 49.0 | 36750 | 1.1164 | 0.8983 |
| 0.0 | 50.0 | 37500 | 1.1181 | 0.8983 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2