vit-base-crack-classification-129
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: 0.4641
- Accuracy: 0.8889
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.3061 | 1.0 | 212 | 1.1094 | 0.6759 |
| 0.844 | 2.0 | 424 | 0.7624 | 0.7940 |
| 0.5972 | 3.0 | 636 | 0.5760 | 0.8472 |
| 0.4424 | 4.0 | 848 | 0.4922 | 0.875 |
| 0.3815 | 5.0 | 1060 | 0.4641 | 0.8889 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0