---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: alzheimer-image-classification-google-vit-base-patch16
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9261006289308176
pipeline_tag: image-classification
---

<!-- 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. -->

# alzheimer-image-classification-google-vit-base-patch16

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: 0.2127
- Accuracy: 0.9261

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8167        | 1.0   | 715  | 0.7520          | 0.6494   |
| 0.6264        | 2.0   | 1431 | 0.6467          | 0.7091   |
| 0.5003        | 3.0   | 2146 | 0.5430          | 0.7594   |
| 0.3543        | 4.0   | 2862 | 0.4372          | 0.8145   |
| 0.3816        | 5.0   | 3577 | 0.3681          | 0.8428   |
| 0.2055        | 6.0   | 4293 | 0.3746          | 0.8514   |
| 0.2526        | 7.0   | 5008 | 0.2836          | 0.8907   |
| 0.1262        | 8.0   | 5724 | 0.2798          | 0.8954   |
| 0.1332        | 9.0   | 6439 | 0.2301          | 0.9159   |
| 0.0702        | 9.99  | 7150 | 0.2127          | 0.9261   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1
- Datasets 2.14.3
- Tokenizers 0.13.3