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---
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
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