
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-huge-binary-isic-sharpened-patch-14

This model is a fine-tuned version of [google/vit-huge-patch14-224-in21k](https://huggingface.co/google/vit-huge-patch14-224-in21k) on the ahishamm/isic_binary_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2129
- Accuracy: 0.9336
- Recall: 0.9336
- F1: 0.9336
- Precision: 0.9336

## 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
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | F1     | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.3116        | 0.09  | 100  | 0.3191          | 0.8553   | 0.8553 | 0.8553 | 0.8553    |
| 0.3328        | 0.18  | 200  | 0.3595          | 0.8152   | 0.8152 | 0.8152 | 0.8152    |
| 0.2597        | 0.28  | 300  | 0.2901          | 0.8733   | 0.8733 | 0.8733 | 0.8733    |
| 0.2037        | 0.37  | 400  | 0.2478          | 0.8912   | 0.8912 | 0.8912 | 0.8912    |
| 0.2415        | 0.46  | 500  | 0.2488          | 0.8966   | 0.8966 | 0.8966 | 0.8966    |
| 0.1483        | 0.55  | 600  | 0.2943          | 0.8844   | 0.8844 | 0.8844 | 0.8844    |
| 0.1126        | 0.65  | 700  | 0.3269          | 0.8752   | 0.8752 | 0.8752 | 0.8752    |
| 0.207         | 0.74  | 800  | 0.2984          | 0.8869   | 0.8869 | 0.8869 | 0.8869    |
| 0.234         | 0.83  | 900  | 0.2394          | 0.8857   | 0.8857 | 0.8857 | 0.8857    |
| 0.1625        | 0.92  | 1000 | 0.2706          | 0.8930   | 0.8930 | 0.8930 | 0.8930    |
| 0.1131        | 1.02  | 1100 | 0.2156          | 0.9208   | 0.9208 | 0.9208 | 0.9208    |
| 0.048         | 1.11  | 1200 | 0.2676          | 0.9026   | 0.9026 | 0.9026 | 0.9026    |
| 0.1235        | 1.2   | 1300 | 0.3326          | 0.9025   | 0.9025 | 0.9025 | 0.9025    |
| 0.0093        | 1.29  | 1400 | 0.3239          | 0.9107   | 0.9107 | 0.9107 | 0.9107    |
| 0.0851        | 1.39  | 1500 | 0.3067          | 0.8971   | 0.8971 | 0.8971 | 0.8971    |
| 0.0962        | 1.48  | 1600 | 0.3164          | 0.8998   | 0.8998 | 0.8998 | 0.8998    |
| 0.0143        | 1.57  | 1700 | 0.2269          | 0.9266   | 0.9266 | 0.9266 | 0.9266    |
| 0.071         | 1.66  | 1800 | 0.3436          | 0.9118   | 0.9118 | 0.9118 | 0.9118    |
| 0.0594        | 1.76  | 1900 | 0.3813          | 0.8997   | 0.8997 | 0.8997 | 0.8997    |
| 0.0406        | 1.85  | 2000 | 0.2129          | 0.9336   | 0.9336 | 0.9336 | 0.9336    |
| 0.048         | 1.94  | 2100 | 0.3117          | 0.9161   | 0.9161 | 0.9161 | 0.9161    |
| 0.0071        | 2.03  | 2200 | 0.2732          | 0.9247   | 0.9247 | 0.9247 | 0.9247    |
| 0.0021        | 2.13  | 2300 | 0.3055          | 0.9275   | 0.9275 | 0.9275 | 0.9275    |
| 0.0722        | 2.22  | 2400 | 0.3442          | 0.9250   | 0.9250 | 0.9250 | 0.9250    |
| 0.0146        | 2.31  | 2500 | 0.2984          | 0.9314   | 0.9314 | 0.9314 | 0.9314    |
| 0.0031        | 2.4   | 2600 | 0.3212          | 0.9298   | 0.9298 | 0.9298 | 0.9298    |
| 0.0167        | 2.5   | 2700 | 0.3111          | 0.9320   | 0.9320 | 0.9320 | 0.9320    |
| 0.0017        | 2.59  | 2800 | 0.2883          | 0.9307   | 0.9307 | 0.9307 | 0.9307    |
| 0.0007        | 2.68  | 2900 | 0.3189          | 0.9321   | 0.9321 | 0.9321 | 0.9321    |
| 0.0012        | 2.77  | 3000 | 0.2996          | 0.9384   | 0.9384 | 0.9384 | 0.9384    |
| 0.0006        | 2.87  | 3100 | 0.3553          | 0.9281   | 0.9281 | 0.9281 | 0.9281    |
| 0.0007        | 2.96  | 3200 | 0.3595          | 0.9285   | 0.9285 | 0.9285 | 0.9285    |
| 0.0238        | 3.05  | 3300 | 0.3351          | 0.9349   | 0.9349 | 0.9349 | 0.9349    |
| 0.0065        | 3.14  | 3400 | 0.3166          | 0.9376   | 0.9376 | 0.9376 | 0.9376    |
| 0.0009        | 3.23  | 3500 | 0.3304          | 0.9331   | 0.9331 | 0.9331 | 0.9331    |
| 0.0006        | 3.33  | 3600 | 0.3215          | 0.9348   | 0.9348 | 0.9348 | 0.9348    |
| 0.0005        | 3.42  | 3700 | 0.3414          | 0.9353   | 0.9353 | 0.9353 | 0.9353    |
| 0.0006        | 3.51  | 3800 | 0.3321          | 0.9350   | 0.9350 | 0.9350 | 0.9350    |
| 0.0006        | 3.6   | 3900 | 0.3306          | 0.9379   | 0.9379 | 0.9379 | 0.9379    |
| 0.0411        | 3.7   | 4000 | 0.3226          | 0.9375   | 0.9375 | 0.9375 | 0.9375    |
| 0.0006        | 3.79  | 4100 | 0.3235          | 0.9377   | 0.9377 | 0.9377 | 0.9377    |
| 0.0354        | 3.88  | 4200 | 0.3268          | 0.9357   | 0.9357 | 0.9357 | 0.9357    |
| 0.0328        | 3.97  | 4300 | 0.3264          | 0.9358   | 0.9358 | 0.9358 | 0.9358    |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3

benign malignant