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# 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: 0.0160
- Accuracy: 0.9969

## 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: 50

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.2996        | 0.9778  | 22   | 0.3897          | 0.8438   |
| 0.3703        | 2.0     | 45   | 0.3595          | 0.8594   |
| 0.3087        | 2.9778  | 67   | 0.3777          | 0.8625   |
| 0.486         | 4.0     | 90   | 0.4530          | 0.8187   |
| 0.3307        | 4.9778  | 112  | 0.4560          | 0.8234   |
| 0.306         | 6.0     | 135  | 0.3471          | 0.8672   |
| 0.3005        | 6.9778  | 157  | 0.3025          | 0.8859   |
| 0.319         | 8.0     | 180  | 0.2451          | 0.8984   |
| 0.3489        | 8.9778  | 202  | 0.1814          | 0.9281   |
| 0.3251        | 10.0    | 225  | 0.2451          | 0.9156   |
| 0.3034        | 10.9778 | 247  | 0.1566          | 0.9406   |
| 0.2746        | 12.0    | 270  | 0.2493          | 0.8922   |
| 0.2369        | 12.9778 | 292  | 0.1622          | 0.9375   |
| 0.2231        | 14.0    | 315  | 0.1781          | 0.9359   |
| 0.2281        | 14.9778 | 337  | 0.1268          | 0.9531   |
| 0.2001        | 16.0    | 360  | 0.2431          | 0.9141   |
| 0.183         | 16.9778 | 382  | 0.1017          | 0.9625   |
| 0.1891        | 18.0    | 405  | 0.1802          | 0.9391   |
| 0.1862        | 18.9778 | 427  | 0.0869          | 0.9766   |
| 0.1935        | 20.0    | 450  | 0.1079          | 0.9688   |
| 0.1797        | 20.9778 | 472  | 0.1250          | 0.9563   |
| 0.1605        | 22.0    | 495  | 0.0655          | 0.9719   |
| 0.1848        | 22.9778 | 517  | 0.0806          | 0.9766   |
| 0.1498        | 24.0    | 540  | 0.1116          | 0.9578   |
| 0.1394        | 24.9778 | 562  | 0.0807          | 0.9672   |
| 0.1584        | 26.0    | 585  | 0.0525          | 0.9797   |
| 0.1302        | 26.9778 | 607  | 0.0513          | 0.9828   |
| 0.1356        | 28.0    | 630  | 0.0420          | 0.9875   |
| 0.1101        | 28.9778 | 652  | 0.0354          | 0.9875   |
| 0.1227        | 30.0    | 675  | 0.0583          | 0.9766   |
| 0.1158        | 30.9778 | 697  | 0.0253          | 0.9906   |
| 0.117         | 32.0    | 720  | 0.0231          | 0.9906   |
| 0.1022        | 32.9778 | 742  | 0.0726          | 0.9797   |
| 0.1221        | 34.0    | 765  | 0.0160          | 0.9969   |
| 0.0956        | 34.9778 | 787  | 0.0482          | 0.9844   |
| 0.0856        | 36.0    | 810  | 0.0256          | 0.9875   |
| 0.0996        | 36.9778 | 832  | 0.0211          | 0.9906   |
| 0.0848        | 38.0    | 855  | 0.0446          | 0.9797   |
| 0.1001        | 38.9778 | 877  | 0.0274          | 0.9875   |
| 0.0976        | 40.0    | 900  | 0.0225          | 0.9922   |
| 0.0864        | 40.9778 | 922  | 0.0207          | 0.9922   |
| 0.0865        | 42.0    | 945  | 0.0193          | 0.9969   |
| 0.0773        | 42.9778 | 967  | 0.0203          | 0.9922   |
| 0.075         | 44.0    | 990  | 0.0131          | 0.9969   |
| 0.0761        | 44.9778 | 1012 | 0.0129          | 0.9938   |
| 0.0624        | 46.0    | 1035 | 0.0114          | 0.9969   |
| 0.0557        | 46.9778 | 1057 | 0.0102          | 0.9953   |
| 0.0708        | 48.0    | 1080 | 0.0116          | 0.9953   |
| 0.0667        | 48.8889 | 1100 | 0.0131          | 0.9953   |


### Framework versions

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

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