---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
- recall
- f1
- precision
model-index:
- name: vit-base-binary-isic-sharpened-patch-32
  results: []
---

<!-- 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-base-binary-isic-sharpened-patch-32

This model is a fine-tuned version of [google/vit-base-patch32-224-in21k](https://huggingface.co/google/vit-base-patch32-224-in21k) on the ahishamm/isic_binary_sharpened dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2620
- Accuracy: 0.8934
- Recall: 0.8934
- F1: 0.8934
- Precision: 0.8934

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.3258        | 0.09  | 100  | 0.4318          | 0.7799   | 0.7799 | 0.7799 | 0.7799    |
| 0.3002        | 0.18  | 200  | 0.3273          | 0.8511   | 0.8511 | 0.8511 | 0.8511    |
| 0.3154        | 0.28  | 300  | 0.3165          | 0.8507   | 0.8507 | 0.8507 | 0.8507    |
| 0.412         | 0.37  | 400  | 0.3700          | 0.8547   | 0.8547 | 0.8547 | 0.8547    |
| 0.3264        | 0.46  | 500  | 0.2831          | 0.8635   | 0.8635 | 0.8635 | 0.8635    |
| 0.2851        | 0.55  | 600  | 0.3020          | 0.8520   | 0.8520 | 0.8520 | 0.8520    |
| 0.2582        | 0.65  | 700  | 0.3071          | 0.8385   | 0.8385 | 0.8385 | 0.8385    |
| 0.2376        | 0.74  | 800  | 0.3013          | 0.8579   | 0.8579 | 0.8579 | 0.8579    |
| 0.2635        | 0.83  | 900  | 0.2909          | 0.8544   | 0.8544 | 0.8544 | 0.8544    |
| 0.2837        | 0.92  | 1000 | 0.3623          | 0.8216   | 0.8216 | 0.8216 | 0.8216    |
| 0.2036        | 1.02  | 1100 | 0.2985          | 0.8763   | 0.8763 | 0.8763 | 0.8763    |
| 0.1586        | 1.11  | 1200 | 0.2620          | 0.8934   | 0.8934 | 0.8934 | 0.8934    |
| 0.1914        | 1.2   | 1300 | 0.2995          | 0.8799   | 0.8799 | 0.8799 | 0.8799    |
| 0.1604        | 1.29  | 1400 | 0.3001          | 0.8839   | 0.8839 | 0.8839 | 0.8839    |
| 0.1788        | 1.39  | 1500 | 0.3013          | 0.8883   | 0.8883 | 0.8883 | 0.8883    |
| 0.1975        | 1.48  | 1600 | 0.3369          | 0.8816   | 0.8816 | 0.8816 | 0.8816    |
| 0.1228        | 1.57  | 1700 | 0.3014          | 0.8835   | 0.8835 | 0.8835 | 0.8835    |
| 0.1982        | 1.66  | 1800 | 0.3094          | 0.8957   | 0.8957 | 0.8957 | 0.8957    |
| 0.1602        | 1.76  | 1900 | 0.3523          | 0.8717   | 0.8717 | 0.8717 | 0.8717    |
| 0.0748        | 1.85  | 2000 | 0.3154          | 0.8889   | 0.8889 | 0.8889 | 0.8889    |
| 0.1385        | 1.94  | 2100 | 0.2992          | 0.8885   | 0.8885 | 0.8885 | 0.8885    |
| 0.0977        | 2.03  | 2200 | 0.2889          | 0.8913   | 0.8913 | 0.8913 | 0.8913    |
| 0.1028        | 2.13  | 2300 | 0.2842          | 0.8967   | 0.8967 | 0.8967 | 0.8967    |
| 0.1025        | 2.22  | 2400 | 0.2997          | 0.8966   | 0.8966 | 0.8966 | 0.8966    |
| 0.0482        | 2.31  | 2500 | 0.3410          | 0.9043   | 0.9043 | 0.9043 | 0.9043    |
| 0.1243        | 2.4   | 2600 | 0.3357          | 0.9044   | 0.9044 | 0.9044 | 0.9044    |
| 0.0591        | 2.5   | 2700 | 0.3079          | 0.9076   | 0.9076 | 0.9076 | 0.9076    |
| 0.0324        | 2.59  | 2800 | 0.3434          | 0.9148   | 0.9148 | 0.9148 | 0.9148    |
| 0.0677        | 2.68  | 2900 | 0.3156          | 0.9083   | 0.9083 | 0.9083 | 0.9083    |
| 0.0397        | 2.77  | 3000 | 0.3390          | 0.9124   | 0.9124 | 0.9124 | 0.9124    |
| 0.0103        | 2.87  | 3100 | 0.3102          | 0.9106   | 0.9106 | 0.9106 | 0.9106    |
| 0.0359        | 2.96  | 3200 | 0.2847          | 0.9134   | 0.9134 | 0.9134 | 0.9134    |
| 0.0073        | 3.05  | 3300 | 0.4039          | 0.9077   | 0.9077 | 0.9077 | 0.9077    |
| 0.0156        | 3.14  | 3400 | 0.3630          | 0.9100   | 0.9100 | 0.9100 | 0.9100    |
| 0.003         | 3.23  | 3500 | 0.3671          | 0.9143   | 0.9143 | 0.9143 | 0.9143    |
| 0.0262        | 3.33  | 3600 | 0.3538          | 0.9152   | 0.9152 | 0.9152 | 0.9152    |
| 0.0035        | 3.42  | 3700 | 0.3822          | 0.9150   | 0.9150 | 0.9150 | 0.9150    |
| 0.0441        | 3.51  | 3800 | 0.3571          | 0.9188   | 0.9188 | 0.9188 | 0.9188    |
| 0.0017        | 3.6   | 3900 | 0.3793          | 0.9157   | 0.9157 | 0.9157 | 0.9157    |
| 0.0471        | 3.7   | 4000 | 0.3491          | 0.9213   | 0.9213 | 0.9213 | 0.9213    |
| 0.0018        | 3.79  | 4100 | 0.3486          | 0.9238   | 0.9238 | 0.9238 | 0.9238    |
| 0.0405        | 3.88  | 4200 | 0.3478          | 0.9248   | 0.9248 | 0.9248 | 0.9248    |
| 0.0231        | 3.97  | 4300 | 0.3471          | 0.9264   | 0.9264 | 0.9264 | 0.9264    |


### Framework versions

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