---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: vit_base_patch16_224-finetuned-SkinDisease
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: image_folder
      type: image_folder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9342629482071713
---

<!-- 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_patch16_224-finetuned-SkinDisease

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1992
- Accuracy: 0.9343

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9099        | 1.0   | 282  | 0.8248          | 0.7647   |
| 0.5848        | 2.0   | 565  | 0.4236          | 0.8748   |
| 0.3952        | 3.0   | 847  | 0.3154          | 0.9021   |
| 0.3957        | 4.0   | 1130 | 0.2695          | 0.9106   |
| 0.3146        | 5.0   | 1412 | 0.2381          | 0.9198   |
| 0.2883        | 6.0   | 1695 | 0.2407          | 0.9218   |
| 0.2264        | 7.0   | 1977 | 0.2160          | 0.9278   |
| 0.2339        | 8.0   | 2260 | 0.2121          | 0.9283   |
| 0.1966        | 9.0   | 2542 | 0.2044          | 0.9303   |
| 0.2366        | 9.98  | 2820 | 0.1992          | 0.9343   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3