---
license: apache-2.0
base_model: facebook/vit-msn-small
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-msn-small-finetuned-alzheimers
  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: 0.8625
---

<!-- 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-msn-small-finetuned-alzheimers

This model is a fine-tuned version of [facebook/vit-msn-small](https://huggingface.co/facebook/vit-msn-small) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3612
- Accuracy: 0.8625

## 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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.9297        | 0.9778 | 22   | 0.8769          | 0.6156   |
| 0.8601        | 2.0    | 45   | 0.7799          | 0.6344   |
| 0.7954        | 2.9778 | 67   | 0.7197          | 0.6828   |
| 0.7468        | 4.0    | 90   | 0.7003          | 0.6734   |
| 0.6935        | 4.9778 | 112  | 0.6064          | 0.7547   |
| 0.6271        | 6.0    | 135  | 0.5648          | 0.7688   |
| 0.5622        | 6.9778 | 157  | 0.4824          | 0.8094   |
| 0.4815        | 8.0    | 180  | 0.4012          | 0.8609   |
| 0.4771        | 8.9778 | 202  | 0.3799          | 0.8562   |
| 0.4171        | 9.7778 | 220  | 0.3612          | 0.8625   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1