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# frost-vision-v2-google_vit-base-patch16-224-v2024-11-09

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: 0.1716
- Accuracy: 0.9412
- F1: 0.8486
- Precision: 0.8540
- Recall: 0.8432

## 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 |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.2398        | 1.4085  | 100  | 0.2096          | 0.9215   | 0.7833 | 0.8502    | 0.7261 |
| 0.1746        | 2.8169  | 200  | 0.1676          | 0.9370   | 0.8362 | 0.8494    | 0.8234 |
| 0.1316        | 4.2254  | 300  | 0.1750          | 0.9282   | 0.8125 | 0.8293    | 0.7964 |
| 0.1305        | 5.6338  | 400  | 0.1671          | 0.9342   | 0.8270 | 0.8498    | 0.8054 |
| 0.1119        | 7.0423  | 500  | 0.1747          | 0.9317   | 0.8240 | 0.8300    | 0.8180 |
| 0.0913        | 8.4507  | 600  | 0.1515          | 0.9415   | 0.8505 | 0.8505    | 0.8505 |
| 0.0964        | 9.8592  | 700  | 0.1680          | 0.9377   | 0.8418 | 0.8351    | 0.8486 |
| 0.0659        | 11.2676 | 800  | 0.1891          | 0.9275   | 0.8144 | 0.8144    | 0.8144 |
| 0.0706        | 12.6761 | 900  | 0.1788          | 0.9320   | 0.8234 | 0.8364    | 0.8108 |
| 0.069         | 14.0845 | 1000 | 0.1716          | 0.9412   | 0.8486 | 0.8540    | 0.8432 |
| 0.0543        | 15.4930 | 1100 | 0.1847          | 0.9363   | 0.8341 | 0.8489    | 0.8198 |
| 0.0515        | 16.9014 | 1200 | 0.1741          | 0.9408   | 0.8470 | 0.8564    | 0.8378 |
| 0.0489        | 18.3099 | 1300 | 0.1793          | 0.9461   | 0.8620 | 0.8628    | 0.8613 |
| 0.0339        | 19.7183 | 1400 | 0.1806          | 0.9444   | 0.8569 | 0.8616    | 0.8523 |
| 0.0409        | 21.1268 | 1500 | 0.1784          | 0.9440   | 0.8569 | 0.8561    | 0.8577 |
| 0.0275        | 22.5352 | 1600 | 0.1839          | 0.9437   | 0.8548 | 0.8611    | 0.8486 |
| 0.0231        | 23.9437 | 1700 | 0.1865          | 0.9415   | 0.8480 | 0.8622    | 0.8342 |
| 0.0204        | 25.3521 | 1800 | 0.1884          | 0.9405   | 0.8482 | 0.8459    | 0.8505 |
| 0.0245        | 26.7606 | 1900 | 0.1935          | 0.9377   | 0.8410 | 0.8387    | 0.8432 |
| 0.0202        | 28.1690 | 2000 | 0.1888          | 0.9394   | 0.8456 | 0.8426    | 0.8486 |
| 0.0187        | 29.5775 | 2100 | 0.1914          | 0.9415   | 0.8502 | 0.8517    | 0.8486 |


### Framework versions

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

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