smids_10x_deit_base_adamax_0001_fold2
This model is a fine-tuned version of facebook/deit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.0292
- Accuracy: 0.8985
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 |
|---|---|---|---|---|
| 0.1628 | 1.0 | 750 | 0.2778 | 0.8885 |
| 0.0865 | 2.0 | 1500 | 0.3575 | 0.9002 |
| 0.0255 | 3.0 | 2250 | 0.5656 | 0.8885 |
| 0.0266 | 4.0 | 3000 | 0.5733 | 0.9002 |
| 0.0169 | 5.0 | 3750 | 0.7037 | 0.9002 |
| 0.0024 | 6.0 | 4500 | 0.6843 | 0.8918 |
| 0.0124 | 7.0 | 5250 | 0.7219 | 0.8985 |
| 0.0002 | 8.0 | 6000 | 0.8057 | 0.8935 |
| 0.002 | 9.0 | 6750 | 0.7964 | 0.8985 |
| 0.0 | 10.0 | 7500 | 0.7467 | 0.9002 |
| 0.0004 | 11.0 | 8250 | 0.6482 | 0.9002 |
| 0.0 | 12.0 | 9000 | 0.8380 | 0.8968 |
| 0.0 | 13.0 | 9750 | 0.8814 | 0.8902 |
| 0.0 | 14.0 | 10500 | 0.8400 | 0.8985 |
| 0.0 | 15.0 | 11250 | 0.7646 | 0.9068 |
| 0.0003 | 16.0 | 12000 | 0.8183 | 0.9068 |
| 0.0 | 17.0 | 12750 | 0.8397 | 0.8985 |
| 0.0 | 18.0 | 13500 | 0.9452 | 0.9052 |
| 0.0 | 19.0 | 14250 | 0.8230 | 0.9085 |
| 0.0 | 20.0 | 15000 | 0.8880 | 0.9085 |
| 0.0 | 21.0 | 15750 | 0.9221 | 0.9035 |
| 0.004 | 22.0 | 16500 | 0.9029 | 0.8968 |
| 0.0026 | 23.0 | 17250 | 0.9288 | 0.9002 |
| 0.0 | 24.0 | 18000 | 0.9054 | 0.9002 |
| 0.0 | 25.0 | 18750 | 0.9636 | 0.8952 |
| 0.0 | 26.0 | 19500 | 0.9715 | 0.8935 |
| 0.0035 | 27.0 | 20250 | 0.9914 | 0.9002 |
| 0.0 | 28.0 | 21000 | 1.0064 | 0.9002 |
| 0.0 | 29.0 | 21750 | 0.9401 | 0.8985 |
| 0.0 | 30.0 | 22500 | 0.9859 | 0.9002 |
| 0.0 | 31.0 | 23250 | 1.0344 | 0.8952 |
| 0.0 | 32.0 | 24000 | 1.0163 | 0.8935 |
| 0.0 | 33.0 | 24750 | 0.9803 | 0.9018 |
| 0.0 | 34.0 | 25500 | 1.0043 | 0.8918 |
| 0.0 | 35.0 | 26250 | 1.0148 | 0.8968 |
| 0.0 | 36.0 | 27000 | 1.0177 | 0.9035 |
| 0.0028 | 37.0 | 27750 | 1.0101 | 0.8985 |
| 0.0 | 38.0 | 28500 | 0.9955 | 0.9018 |
| 0.0 | 39.0 | 29250 | 1.0034 | 0.8985 |
| 0.0 | 40.0 | 30000 | 1.0043 | 0.8985 |
| 0.0 | 41.0 | 30750 | 0.9850 | 0.9002 |
| 0.0 | 42.0 | 31500 | 0.9989 | 0.8985 |
| 0.0 | 43.0 | 32250 | 1.0060 | 0.8985 |
| 0.0 | 44.0 | 33000 | 1.0153 | 0.8985 |
| 0.0024 | 45.0 | 33750 | 1.0191 | 0.8985 |
| 0.0 | 46.0 | 34500 | 1.0252 | 0.8985 |
| 0.0 | 47.0 | 35250 | 1.0250 | 0.8985 |
| 0.0 | 48.0 | 36000 | 1.0256 | 0.8985 |
| 0.0 | 49.0 | 36750 | 1.0285 | 0.8985 |
| 0.0 | 50.0 | 37500 | 1.0292 | 0.8985 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2