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vit-base-riego
This model is a fine-tuned version of google/vit-base-patch16-224-in21k on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.2998
- F1: 0.3729
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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|---|---|---|---|---|
| 0.1696 | 0.79 | 100 | 1.1385 | 0.352 |
| 0.08 | 1.59 | 200 | 0.9071 | 0.3774 |
| 0.0928 | 2.38 | 300 | 1.1181 | 0.3454 |
| 0.0189 | 3.17 | 400 | 0.8262 | 0.3425 |
| 0.0728 | 3.97 | 500 | 0.9647 | 0.3747 |
| 0.0756 | 4.76 | 600 | 0.6097 | 0.4776 |
| 0.0018 | 5.56 | 700 | 1.3900 | 0.3652 |
| 0.002 | 6.35 | 800 | 0.7498 | 0.4606 |
| 0.0304 | 7.14 | 900 | 1.4367 | 0.3666 |
| 0.0024 | 7.94 | 1000 | 1.5714 | 0.3041 |
| 0.0463 | 8.73 | 1100 | 0.8038 | 0.4016 |
| 0.0014 | 9.52 | 1200 | 0.7175 | 0.4795 |
| 0.0015 | 10.32 | 1300 | 1.0347 | 0.3959 |
| 0.0009 | 11.11 | 1400 | 1.3881 | 0.3670 |
| 0.0131 | 11.9 | 1500 | 1.0780 | 0.4044 |
| 0.0007 | 12.7 | 1600 | 0.9834 | 0.4255 |
| 0.0011 | 13.49 | 1700 | 1.0753 | 0.4033 |
| 0.0007 | 14.29 | 1800 | 1.1514 | 0.3989 |
| 0.0007 | 15.08 | 1900 | 1.2373 | 0.3769 |
| 0.0007 | 15.87 | 2000 | 1.2998 | 0.3729 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
