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---
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: 0.996875
---
<!-- 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: 0.0160
- 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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