---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-brain-mri-dementia-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-mri-dementia-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: 0.1089
- Accuracy: 0.9789

## 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: 0.0002
- 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
- num_epochs: 20
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.8826        | 0.3125  | 100  | 0.9027          | 0.575    |
| 0.8908        | 0.625   | 200  | 0.8484          | 0.5984   |
| 0.8229        | 0.9375  | 300  | 0.7514          | 0.6695   |
| 0.5299        | 1.25    | 400  | 0.6798          | 0.7164   |
| 0.5207        | 1.5625  | 500  | 0.6466          | 0.7375   |
| 0.4967        | 1.875   | 600  | 0.6303          | 0.7461   |
| 0.3977        | 2.1875  | 700  | 0.7240          | 0.7719   |
| 0.2744        | 2.5     | 800  | 0.3544          | 0.8734   |
| 0.4271        | 2.8125  | 900  | 0.3037          | 0.8938   |
| 0.2484        | 3.125   | 1000 | 0.4111          | 0.8602   |
| 0.0797        | 3.4375  | 1100 | 0.3782          | 0.8953   |
| 0.0662        | 3.75    | 1200 | 0.3096          | 0.9172   |
| 0.0894        | 4.0625  | 1300 | 0.2818          | 0.9289   |
| 0.1005        | 4.375   | 1400 | 0.2164          | 0.9469   |
| 0.0997        | 4.6875  | 1500 | 0.3378          | 0.9109   |
| 0.0715        | 5.0     | 1600 | 0.3627          | 0.9133   |
| 0.0567        | 5.3125  | 1700 | 0.3061          | 0.9234   |
| 0.0558        | 5.625   | 1800 | 0.2393          | 0.9461   |
| 0.0061        | 5.9375  | 1900 | 0.1738          | 0.9586   |
| 0.0449        | 6.25    | 2000 | 0.2094          | 0.9492   |
| 0.0073        | 6.5625  | 2100 | 0.1834          | 0.9539   |
| 0.0425        | 6.875   | 2200 | 0.2847          | 0.9266   |
| 0.0397        | 7.1875  | 2300 | 0.4031          | 0.9125   |
| 0.0284        | 7.5     | 2400 | 0.2995          | 0.9406   |
| 0.0158        | 7.8125  | 2500 | 0.1909          | 0.9664   |
| 0.006         | 8.125   | 2600 | 0.3524          | 0.9297   |
| 0.0017        | 8.4375  | 2700 | 0.1908          | 0.9617   |
| 0.0026        | 8.75    | 2800 | 0.1787          | 0.9625   |
| 0.001         | 9.0625  | 2900 | 0.1329          | 0.9688   |
| 0.0497        | 9.375   | 3000 | 0.1878          | 0.9594   |
| 0.09          | 9.6875  | 3100 | 0.1754          | 0.9648   |
| 0.0046        | 10.0    | 3200 | 0.1584          | 0.9672   |
| 0.0006        | 10.3125 | 3300 | 0.2008          | 0.9648   |
| 0.0008        | 10.625  | 3400 | 0.1272          | 0.975    |
| 0.028         | 10.9375 | 3500 | 0.1453          | 0.9766   |
| 0.0005        | 11.25   | 3600 | 0.1256          | 0.975    |
| 0.0005        | 11.5625 | 3700 | 0.1089          | 0.9789   |
| 0.0004        | 11.875  | 3800 | 0.1098          | 0.9781   |
| 0.0003        | 12.1875 | 3900 | 0.1779          | 0.9625   |
| 0.0163        | 12.5    | 4000 | 0.2500          | 0.9539   |
| 0.0003        | 12.8125 | 4100 | 0.1556          | 0.9734   |
| 0.0003        | 13.125  | 4200 | 0.1205          | 0.9742   |
| 0.0002        | 13.4375 | 4300 | 0.1543          | 0.9719   |
| 0.0002        | 13.75   | 4400 | 0.1548          | 0.975    |
| 0.0003        | 14.0625 | 4500 | 0.1497          | 0.975    |
| 0.0002        | 14.375  | 4600 | 0.2317          | 0.9641   |
| 0.0003        | 14.6875 | 4700 | 0.1418          | 0.9781   |
| 0.0002        | 15.0    | 4800 | 0.1537          | 0.9734   |
| 0.0002        | 15.3125 | 4900 | 0.1426          | 0.9781   |
| 0.0002        | 15.625  | 5000 | 0.1253          | 0.9820   |
| 0.0002        | 15.9375 | 5100 | 0.1128          | 0.9836   |
| 0.0002        | 16.25   | 5200 | 0.1246          | 0.9805   |
| 0.0002        | 16.5625 | 5300 | 0.1137          | 0.9828   |
| 0.0001        | 16.875  | 5400 | 0.1101          | 0.9844   |
| 0.0001        | 17.1875 | 5500 | 0.1112          | 0.9844   |
| 0.0001        | 17.5    | 5600 | 0.1121          | 0.9844   |
| 0.0001        | 17.8125 | 5700 | 0.1129          | 0.9836   |
| 0.0001        | 18.125  | 5800 | 0.1135          | 0.9844   |
| 0.0001        | 18.4375 | 5900 | 0.1140          | 0.9844   |
| 0.0001        | 18.75   | 6000 | 0.1146          | 0.9844   |
| 0.0001        | 19.0625 | 6100 | 0.1150          | 0.9844   |
| 0.0001        | 19.375  | 6200 | 0.1153          | 0.9844   |
| 0.0001        | 19.6875 | 6300 | 0.1155          | 0.9844   |
| 0.0001        | 20.0    | 6400 | 0.1155          | 0.9844   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1