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# Alzheimer_classification_model

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: 0.4065
- Accuracy: 0.8375

## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.692         | 1.0   | 80   | 0.8592          | 0.6258   |
| 0.662         | 2.0   | 160  | 0.7454          | 0.6781   |
| 0.6124        | 3.0   | 240  | 0.6895          | 0.6922   |
| 0.5851        | 4.0   | 320  | 0.6332          | 0.7430   |
| 0.5495        | 5.0   | 400  | 0.5804          | 0.7586   |
| 0.4334        | 6.0   | 480  | 0.6068          | 0.7484   |
| 0.4169        | 7.0   | 560  | 0.5168          | 0.7883   |
| 0.3709        | 8.0   | 640  | 0.4768          | 0.8055   |
| 0.2854        | 9.0   | 720  | 0.4641          | 0.8117   |
| 0.3064        | 10.0  | 800  | 0.4065          | 0.8375   |


### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.1+cu118
- Datasets 2.9.0
- Tokenizers 0.13.3

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