| --- |
| license: apache-2.0 |
| base_model: google/vit-base-patch16-224 |
| tags: |
| - generated_from_trainer |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: vit-augmentation |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # vit-augmentation |
|
|
| This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5668 |
| - Accuracy: 0.8804 |
| - Precision: 0.8823 |
| - Recall: 0.8804 |
| - F1: 0.8789 |
|
|
| ## 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: 16 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 770 |
| - num_epochs: 100 |
| - mixed_precision_training: Native AMP |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.9124 | 1.0 | 321 | 0.6025 | 0.7805 | 0.7788 | 0.7805 | 0.7683 | |
| | 0.5876 | 2.0 | 642 | 0.5819 | 0.7864 | 0.7990 | 0.7864 | 0.7820 | |
| | 0.5415 | 3.0 | 963 | 0.6149 | 0.8041 | 0.7943 | 0.8041 | 0.7865 | |
| | 0.4815 | 4.0 | 1284 | 0.4654 | 0.8294 | 0.8259 | 0.8294 | 0.8115 | |
| | 0.4263 | 5.0 | 1605 | 0.5481 | 0.8259 | 0.8315 | 0.8259 | 0.8023 | |
| | 0.3515 | 6.0 | 1926 | 0.4287 | 0.8592 | 0.8580 | 0.8592 | 0.8574 | |
| | 0.3144 | 7.0 | 2247 | 0.5005 | 0.8363 | 0.8320 | 0.8363 | 0.8270 | |
| | 0.2736 | 8.0 | 2568 | 0.5306 | 0.8294 | 0.8448 | 0.8294 | 0.8302 | |
| | 0.2519 | 9.0 | 2889 | 0.4733 | 0.8578 | 0.8534 | 0.8578 | 0.8534 | |
| | 0.2227 | 10.0 | 3210 | 0.4905 | 0.8585 | 0.8520 | 0.8585 | 0.8512 | |
| | 0.1724 | 11.0 | 3531 | 0.5050 | 0.8655 | 0.8671 | 0.8655 | 0.8628 | |
| | 0.1596 | 12.0 | 3852 | 0.5263 | 0.8686 | 0.8657 | 0.8686 | 0.8631 | |
| | 0.1397 | 13.0 | 4173 | 0.7043 | 0.8533 | 0.8703 | 0.8533 | 0.8488 | |
| | 0.1298 | 14.0 | 4494 | 0.6275 | 0.8679 | 0.8734 | 0.8679 | 0.8632 | |
| | 0.1029 | 15.0 | 4815 | 0.5564 | 0.8807 | 0.8776 | 0.8807 | 0.8772 | |
| | 0.0893 | 16.0 | 5136 | 0.5668 | 0.8804 | 0.8823 | 0.8804 | 0.8789 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.40.0.dev0 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
| |
