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# frost-vision-v2-google_vit-base-patch16-384-v2024-11-10

This model is a fine-tuned version of [google/vit-base-patch16-384](https://huggingface.co/google/vit-base-patch16-384) on the webdataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1847
- Accuracy: 0.9275
- F1: 0.8187
- Precision: 0.8172
- Recall: 0.8201

## 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
- 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2243        | 1.4085  | 100  | 0.2088          | 0.9243   | 0.7981 | 0.8534    | 0.7496 |
| 0.2438        | 2.8169  | 200  | 0.1819          | 0.9299   | 0.8103 | 0.8817    | 0.7496 |
| 0.1338        | 4.2254  | 300  | 0.1608          | 0.9377   | 0.8449 | 0.8397    | 0.8501 |
| 0.1224        | 5.6338  | 400  | 0.1735          | 0.9271   | 0.8179 | 0.8158    | 0.8201 |
| 0.1065        | 7.0423  | 500  | 0.1847          | 0.9275   | 0.8187 | 0.8172    | 0.8201 |
| 0.1008        | 8.4507  | 600  | 0.1710          | 0.9405   | 0.8506 | 0.8528    | 0.8483 |
| 0.1005        | 9.8592  | 700  | 0.1823          | 0.9384   | 0.8405 | 0.8698    | 0.8131 |
| 0.0756        | 11.2676 | 800  | 0.1771          | 0.9415   | 0.8520 | 0.8613    | 0.8430 |
| 0.0653        | 12.6761 | 900  | 0.1971          | 0.9324   | 0.8310 | 0.8295    | 0.8325 |
| 0.0367        | 14.0845 | 1000 | 0.2123          | 0.9296   | 0.8221 | 0.8294    | 0.8148 |
| 0.0459        | 15.4930 | 1100 | 0.2006          | 0.9335   | 0.832  | 0.8387    | 0.8254 |
| 0.0559        | 16.9014 | 1200 | 0.2097          | 0.9313   | 0.8232 | 0.8470    | 0.8007 |
| 0.0382        | 18.3099 | 1300 | 0.2055          | 0.9352   | 0.8372 | 0.8401    | 0.8342 |
| 0.0361        | 19.7183 | 1400 | 0.2070          | 0.9335   | 0.8305 | 0.8449    | 0.8166 |
| 0.0358        | 21.1268 | 1500 | 0.1959          | 0.9398   | 0.8458 | 0.8653    | 0.8272 |
| 0.0382        | 22.5352 | 1600 | 0.2097          | 0.9320   | 0.8269 | 0.8412    | 0.8131 |
| 0.0285        | 23.9437 | 1700 | 0.2016          | 0.9415   | 0.8515 | 0.8639    | 0.8395 |
| 0.0141        | 25.3521 | 1800 | 0.2161          | 0.9366   | 0.8384 | 0.8537    | 0.8236 |
| 0.0179        | 26.7606 | 1900 | 0.2073          | 0.9377   | 0.8427 | 0.8495    | 0.8360 |
| 0.0263        | 28.1690 | 2000 | 0.2097          | 0.9391   | 0.8457 | 0.8556    | 0.8360 |
| 0.0191        | 29.5775 | 2100 | 0.2101          | 0.9377   | 0.8415 | 0.8545    | 0.8289 |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.5.0+cu121
- Datasets 3.1.0
- Tokenizers 0.19.1

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