---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21K
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: finetuned-skinpics
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.5138888888888888
---

<!-- 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. -->

# finetuned-skinpics

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2540
- Accuracy: 0.5139

## 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: 7

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.121         | 0.57  | 100  | 1.1020          | 0.2569   |
| 1.0768        | 1.15  | 200  | 1.0546          | 0.4792   |
| 1.0532        | 1.72  | 300  | 1.0843          | 0.2917   |
| 1.0096        | 2.3   | 400  | 1.0693          | 0.4792   |
| 1.0716        | 2.87  | 500  | 1.0466          | 0.4931   |
| 1.0346        | 3.45  | 600  | 1.0225          | 0.5139   |
| 1.0232        | 4.02  | 700  | 1.0230          | 0.4931   |
| 0.8936        | 4.6   | 800  | 1.0582          | 0.5069   |
| 0.7125        | 5.17  | 900  | 1.0551          | 0.5139   |
| 0.6025        | 5.75  | 1000 | 1.1525          | 0.5278   |
| 0.4663        | 6.32  | 1100 | 1.2357          | 0.4653   |
| 0.5007        | 6.9   | 1200 | 1.2540          | 0.5139   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2