vit-base-crack-classification-aug
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0165
- Accuracy: 0.9907
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: 0.0002
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.4964 | 1.0 | 212 | 0.3400 | 0.8796 |
| 0.249 | 2.0 | 424 | 0.1651 | 0.9236 |
| 0.1216 | 3.0 | 636 | 0.0585 | 0.9676 |
| 0.0488 | 4.0 | 848 | 0.0382 | 0.9769 |
| 0.0304 | 5.0 | 1060 | 0.0302 | 0.9907 |
| 0.0107 | 6.0 | 1272 | 0.0294 | 0.9838 |
| 0.0093 | 7.0 | 1484 | 0.0165 | 0.9907 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0