test-cifar-10
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: 1.9675
- Accuracy: 0.1471
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: 2e-05
- train_batch_size: 10
- eval_batch_size: 4
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 12
Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|---|---|---|---|---|
| No log | 1.0 | 398 | 0.1078 | 2.4878 |
| 2.6367 | 2.0 | 796 | 0.1225 | 2.2750 |
| 2.0748 | 3.0 | 1194 | 0.1471 | 2.1435 |
| 1.9035 | 4.0 | 1592 | 0.1225 | 2.0770 |
| 1.9035 | 5.0 | 1990 | 0.1422 | 2.0976 |
| 1.8217 | 6.0 | 2388 | 0.1618 | 1.9768 |
| 1.7998 | 7.0 | 2786 | 2.0803 | 0.1275 |
| 1.7268 | 8.0 | 3184 | 1.9141 | 0.1569 |
| 1.6826 | 9.0 | 3582 | 1.7059 | 0.2010 |
| 1.6826 | 10.0 | 3980 | 2.0650 | 0.1127 |
| 1.6642 | 11.0 | 4378 | 1.9643 | 0.1520 |
| 1.6267 | 12.0 | 4776 | 1.9675 | 0.1471 |
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
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Base model
google/vit-base-patch16-224-in21k