---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- skin-lesion
- dermatology
- vit
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-ham10000
  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-ham10000

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the HAM10000 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5809
- Accuracy: 0.7848

## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6898        | 1.0   | 501  | 0.6788          | 0.7474   |
| 0.5914        | 2.0   | 1002 | 0.6237          | 0.7664   |
| 0.6228        | 3.0   | 1503 | 0.6005          | 0.7763   |
| 0.5843        | 4.0   | 2004 | 0.5855          | 0.7848   |
| 0.5569        | 5.0   | 2505 | 0.5809          | 0.7848   |


### Framework versions

- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2