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# vit-lr-cosine-warm-restarts

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: 0.6693
- Accuracy: 0.7663
- Precision: 0.7457
- Recall: 0.7663
- F1: 0.7516

## 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: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.799         | 0.31  | 100  | 0.8373          | 0.7049   | 0.7500    | 0.7049 | 0.6939 |
| 0.8436        | 0.62  | 200  | 0.9703          | 0.6931   | 0.5695    | 0.6931 | 0.6003 |
| 1.132         | 0.93  | 300  | 1.0145          | 0.6526   | 0.7095    | 0.6526 | 0.6426 |
| 0.7933        | 1.25  | 400  | 1.0078          | 0.6221   | 0.7315    | 0.6221 | 0.6360 |
| 0.7545        | 1.56  | 500  | 0.8741          | 0.7129   | 0.6682    | 0.7129 | 0.6351 |
| 0.9072        | 1.87  | 600  | 0.7939          | 0.7441   | 0.7069    | 0.7441 | 0.7185 |
| 0.6598        | 2.18  | 700  | 0.7062          | 0.7462   | 0.7352    | 0.7462 | 0.7246 |
| 0.794         | 2.49  | 800  | 0.7550          | 0.7132   | 0.7302    | 0.7132 | 0.7036 |
| 0.7198        | 2.8   | 900  | 0.7468          | 0.7198   | 0.7209    | 0.7198 | 0.6999 |
| 0.5423        | 3.12  | 1000 | 0.6507          | 0.7632   | 0.7412    | 0.7632 | 0.7437 |
| 0.4613        | 3.43  | 1100 | 0.6949          | 0.7531   | 0.7206    | 0.7531 | 0.7194 |
| 0.6132        | 3.74  | 1200 | 0.7173          | 0.7434   | 0.7388    | 0.7434 | 0.7244 |
| 0.6514        | 4.05  | 1300 | 0.6862          | 0.7462   | 0.7387    | 0.7462 | 0.7389 |
| 1.0432        | 4.36  | 1400 | 0.7213          | 0.7181   | 0.7497    | 0.7181 | 0.7225 |
| 0.6333        | 4.67  | 1500 | 0.6881          | 0.7552   | 0.7393    | 0.7552 | 0.7268 |
| 0.4887        | 4.98  | 1600 | 0.6634          | 0.7569   | 0.7233    | 0.7569 | 0.7215 |
| 0.6725        | 5.3   | 1700 | 0.6919          | 0.7448   | 0.7625    | 0.7448 | 0.7445 |
| 0.7279        | 5.61  | 1800 | 0.8664          | 0.7250   | 0.6968    | 0.7250 | 0.6635 |
| 0.5567        | 5.92  | 1900 | 0.7133          | 0.7347   | 0.7382    | 0.7347 | 0.6940 |
| 0.4732        | 6.23  | 2000 | 0.6693          | 0.7663   | 0.7457    | 0.7663 | 0.7516 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

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