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# vit-weight-decay-1e-5

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the skin-cancer dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4632
- Accuracy: 0.8460
- Precision: 0.8510
- Recall: 0.8460
- F1: 0.8480

## 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: 1733
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.7624        | 1.0   | 321  | 0.9686          | 0.7077   | 0.6754    | 0.7077 | 0.6680 |
| 1.1455        | 2.0   | 642  | 0.7167          | 0.7340   | 0.7348    | 0.7340 | 0.7184 |
| 1.0313        | 3.0   | 963  | 0.6458          | 0.7583   | 0.7586    | 0.7583 | 0.7305 |
| 0.9864        | 4.0   | 1284 | 0.5631          | 0.7774   | 0.7907    | 0.7774 | 0.7821 |
| 0.931         | 5.0   | 1605 | 0.5847          | 0.7850   | 0.7882    | 0.7850 | 0.7784 |
| 0.9641        | 6.0   | 1926 | 0.5276          | 0.7899   | 0.7935    | 0.7899 | 0.7906 |
| 0.8935        | 7.0   | 2247 | 0.7242          | 0.7226   | 0.7970    | 0.7226 | 0.7430 |
| 0.7589        | 8.0   | 2568 | 0.6404          | 0.7445   | 0.7985    | 0.7445 | 0.7604 |
| 0.7225        | 9.0   | 2889 | 0.5415          | 0.7975   | 0.8100    | 0.7975 | 0.7986 |
| 0.6964        | 10.0  | 3210 | 0.5357          | 0.7871   | 0.8323    | 0.7871 | 0.8009 |
| 0.6232        | 11.0  | 3531 | 0.5579          | 0.8003   | 0.8272    | 0.8003 | 0.8084 |
| 0.5781        | 12.0  | 3852 | 0.6126          | 0.7847   | 0.8315    | 0.7847 | 0.7978 |
| 0.4713        | 13.0  | 4173 | 0.6180          | 0.8259   | 0.8343    | 0.8259 | 0.8161 |
| 0.4834        | 14.0  | 4494 | 0.5668          | 0.8096   | 0.8426    | 0.8096 | 0.8181 |
| 0.3886        | 15.0  | 4815 | 0.4632          | 0.8460   | 0.8510    | 0.8460 | 0.8480 |
| 0.3654        | 16.0  | 5136 | 0.6023          | 0.8065   | 0.8375    | 0.8065 | 0.8168 |
| 0.2904        | 17.0  | 5457 | 0.5002          | 0.8537   | 0.8626    | 0.8537 | 0.8558 |
| 0.2865        | 18.0  | 5778 | 0.5731          | 0.8332   | 0.8583    | 0.8332 | 0.8408 |
| 0.2122        | 19.0  | 6099 | 0.6130          | 0.8325   | 0.8606    | 0.8325 | 0.8411 |
| 0.2227        | 20.0  | 6420 | 0.6097          | 0.8485   | 0.8531    | 0.8485 | 0.8494 |
| 0.179         | 21.0  | 6741 | 0.5650          | 0.8693   | 0.8633    | 0.8693 | 0.8639 |
| 0.1257        | 22.0  | 7062 | 0.5759          | 0.8714   | 0.8712    | 0.8714 | 0.8707 |
| 0.1265        | 23.0  | 7383 | 0.6089          | 0.8710   | 0.8684    | 0.8710 | 0.8688 |
| 0.1146        | 24.0  | 7704 | 0.6169          | 0.8769   | 0.8737    | 0.8769 | 0.8744 |
| 0.1368        | 25.0  | 8025 | 0.5994          | 0.8745   | 0.8743    | 0.8745 | 0.8739 |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

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