---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-brain-alzheimer-detection
  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-brain-alzheimer-detection

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2301
- Accuracy: 0.9555

## 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: 2e-05
- 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
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.4285        | 1.9531  | 500  | 0.4633          | 0.8311   |
| 0.171         | 3.9062  | 1000 | 0.3237          | 0.8994   |
| 0.0622        | 5.8594  | 1500 | 0.2032          | 0.9414   |
| 0.0162        | 7.8125  | 2000 | 0.2413          | 0.9512   |
| 0.0044        | 9.7656  | 2500 | 0.1623          | 0.9668   |
| 0.003         | 11.7188 | 3000 | 0.1641          | 0.9668   |
| 0.0025        | 13.6719 | 3500 | 0.1796          | 0.9619   |
| 0.0019        | 15.625  | 4000 | 0.1892          | 0.9590   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1