---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Melanoma-Classification
  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. -->

# Melanoma-Classification

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the [SeyedAli/Skin-Lesion-Dataset](https://huggingface.co/datasets/SeyedAli/Skin-Lesion-Dataset) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5750
- Accuracy: 0.8167

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9779        | 0.08  | 100  | 1.1158          | 0.6041   |
| 0.9934        | 0.16  | 200  | 1.0227          | 0.6501   |
| 0.9562        | 0.24  | 300  | 0.9276          | 0.6748   |
| 1.0995        | 0.32  | 400  | 0.9088          | 0.6836   |
| 0.8198        | 0.39  | 500  | 0.8581          | 0.6949   |
| 0.8034        | 0.47  | 600  | 0.8444          | 0.6967   |
| 0.8319        | 0.55  | 700  | 0.8196          | 0.7148   |
| 0.787         | 0.63  | 800  | 0.8360          | 0.6975   |
| 0.8642        | 0.71  | 900  | 0.8250          | 0.7008   |
| 0.8329        | 0.79  | 1000 | 0.7939          | 0.7172   |
| 0.9678        | 0.87  | 1100 | 0.7661          | 0.7332   |
| 0.8226        | 0.95  | 1200 | 0.7284          | 0.7373   |
| 0.7967        | 1.03  | 1300 | 0.7355          | 0.7411   |
| 0.6531        | 1.1   | 1400 | 0.7561          | 0.7247   |
| 0.5719        | 1.18  | 1500 | 0.6839          | 0.7638   |
| 0.6123        | 1.26  | 1600 | 0.6857          | 0.7584   |
| 0.6504        | 1.34  | 1700 | 0.6970          | 0.7531   |
| 0.6214        | 1.42  | 1800 | 0.6841          | 0.7576   |
| 0.4925        | 1.5   | 1900 | 0.6624          | 0.7642   |
| 0.5797        | 1.58  | 2000 | 0.6287          | 0.7709   |
| 0.6018        | 1.66  | 2100 | 0.6537          | 0.7622   |
| 0.6334        | 1.74  | 2200 | 0.6413          | 0.7713   |
| 0.4111        | 1.82  | 2300 | 0.6242          | 0.7786   |
| 0.4779        | 1.89  | 2400 | 0.6260          | 0.7790   |
| 0.5488        | 1.97  | 2500 | 0.6146          | 0.7807   |
| 0.3212        | 2.05  | 2600 | 0.6975          | 0.7707   |
| 0.4282        | 2.13  | 2700 | 0.6344          | 0.7790   |
| 0.2822        | 2.21  | 2800 | 0.6985          | 0.7845   |
| 0.3003        | 2.29  | 2900 | 0.5954          | 0.7993   |
| 0.2982        | 2.37  | 3000 | 0.6156          | 0.7940   |
| 0.2628        | 2.45  | 3100 | 0.6318          | 0.7963   |
| 0.2987        | 2.53  | 3200 | 0.6495          | 0.8030   |
| 0.2714        | 2.6   | 3300 | 0.6018          | 0.8052   |
| 0.3059        | 2.68  | 3400 | 0.5944          | 0.8078   |
| 0.2762        | 2.76  | 3500 | 0.6296          | 0.7936   |
| 0.3685        | 2.84  | 3600 | 0.6277          | 0.8017   |
| 0.2299        | 2.92  | 3700 | 0.5834          | 0.8125   |
| 0.3414        | 3.0   | 3800 | 0.5750          | 0.8167   |
| 0.1082        | 3.08  | 3900 | 0.6201          | 0.8196   |
| 0.049         | 3.16  | 4000 | 0.6475          | 0.8161   |
| 0.102         | 3.24  | 4100 | 0.6791          | 0.8097   |
| 0.0483        | 3.31  | 4200 | 0.6582          | 0.8216   |
| 0.1204        | 3.39  | 4300 | 0.6603          | 0.8222   |
| 0.0611        | 3.47  | 4400 | 0.7174          | 0.8190   |
| 0.0555        | 3.55  | 4500 | 0.6841          | 0.8236   |
| 0.0188        | 3.63  | 4600 | 0.7009          | 0.8240   |
| 0.1292        | 3.71  | 4700 | 0.7040          | 0.8204   |
| 0.0661        | 3.79  | 4800 | 0.7074          | 0.8238   |
| 0.1061        | 3.87  | 4900 | 0.6984          | 0.8210   |
| 0.0861        | 3.95  | 5000 | 0.6913          | 0.8230   |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2