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# frost-vision-v2-google_vit-base-patch16-224-v2024-11-14

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the webdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1577
- Accuracy: 0.9389
- F1: 0.8436
- Precision: 0.8655
- Recall: 0.8228

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.3381        | 1.2346  | 100  | 0.3271          | 0.8660   | 0.5669 | 0.8045    | 0.4376 |
| 0.2067        | 2.4691  | 200  | 0.2080          | 0.9194   | 0.7827 | 0.8514    | 0.7242 |
| 0.1745        | 3.7037  | 300  | 0.1864          | 0.9228   | 0.8003 | 0.8308    | 0.7720 |
| 0.1724        | 4.9383  | 400  | 0.1792          | 0.9299   | 0.8188 | 0.8493    | 0.7904 |
| 0.128         | 6.1728  | 500  | 0.1736          | 0.9327   | 0.8292 | 0.8437    | 0.8151 |
| 0.1034        | 7.4074  | 600  | 0.1672          | 0.9355   | 0.8348 | 0.8571    | 0.8136 |
| 0.0944        | 8.6420  | 700  | 0.1579          | 0.9392   | 0.8452 | 0.8622    | 0.8290 |
| 0.0919        | 9.8765  | 800  | 0.1631          | 0.9364   | 0.8347 | 0.8710    | 0.8012 |
| 0.0791        | 11.1111 | 900  | 0.1592          | 0.9380   | 0.8383 | 0.8771    | 0.8028 |
| 0.0684        | 12.3457 | 1000 | 0.1577          | 0.9389   | 0.8436 | 0.8655    | 0.8228 |
| 0.0737        | 13.5802 | 1100 | 0.1678          | 0.9380   | 0.8416 | 0.8613    | 0.8228 |
| 0.0625        | 14.8148 | 1200 | 0.1646          | 0.9426   | 0.8542 | 0.8692    | 0.8398 |
| 0.0591        | 16.0494 | 1300 | 0.1625          | 0.9432   | 0.8549 | 0.8756    | 0.8351 |
| 0.0464        | 17.2840 | 1400 | 0.1722          | 0.9386   | 0.8422 | 0.8676    | 0.8182 |
| 0.048         | 18.5185 | 1500 | 0.1694          | 0.9401   | 0.8472 | 0.8663    | 0.8290 |
| 0.0353        | 19.7531 | 1600 | 0.1715          | 0.9392   | 0.8462 | 0.8576    | 0.8351 |
| 0.0434        | 20.9877 | 1700 | 0.1817          | 0.9370   | 0.8386 | 0.8618    | 0.8166 |
| 0.0332        | 22.2222 | 1800 | 0.1797          | 0.9383   | 0.8423 | 0.8627    | 0.8228 |
| 0.0283        | 23.4568 | 1900 | 0.1810          | 0.9401   | 0.8482 | 0.8617    | 0.8351 |
| 0.0474        | 24.6914 | 2000 | 0.1765          | 0.9398   | 0.8454 | 0.8709    | 0.8213 |
| 0.0365        | 25.9259 | 2100 | 0.1835          | 0.9414   | 0.8516 | 0.8637    | 0.8398 |
| 0.0244        | 27.1605 | 2200 | 0.1822          | 0.9404   | 0.8479 | 0.8677    | 0.8290 |
| 0.0242        | 28.3951 | 2300 | 0.1808          | 0.9407   | 0.8483 | 0.8703    | 0.8274 |
| 0.0296        | 29.6296 | 2400 | 0.1817          | 0.9401   | 0.8477 | 0.864     | 0.8320 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3

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