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---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: alz-mri-vit
results:
- task:
name: image-classification
type: image-classification
dataset:
name: Falah/Alzheimer_MRI
type: Falah/Alzheimer_MRI
config: default
split: train
args: default
metrics:
- name: f1
type: f1
value: 0.930865
datasets:
- Falah/Alzheimer_MRI
---
<!-- 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. -->
# alz-mri-vit
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on Falah/Alzheimer_MRI dataset (fine-tuning procedure is described [here](https://huggingface.co/spolivin/alz-mri-vit/blob/main/vit_finetuning.ipynb)).
It achieves the following results on the evaluation set:
- Loss: 0.1875
- F1: 0.9309
## 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: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1218 | 1.0 | 64 | 0.9419 | 0.5742 |
| 0.94 | 2.0 | 128 | 0.9054 | 0.6029 |
| 0.9123 | 3.0 | 192 | 0.9019 | 0.5262 |
| 0.8625 | 4.0 | 256 | 0.8465 | 0.6029 |
| 0.8104 | 5.0 | 320 | 0.7810 | 0.6319 |
| 0.7244 | 6.0 | 384 | 0.7278 | 0.7037 |
| 0.697 | 7.0 | 448 | 0.6300 | 0.7480 |
| 0.5865 | 8.0 | 512 | 0.5659 | 0.7662 |
| 0.5199 | 9.0 | 576 | 0.5445 | 0.7721 |
| 0.4734 | 10.0 | 640 | 0.6750 | 0.7185 |
| 0.4399 | 11.0 | 704 | 0.4893 | 0.8274 |
| 0.3817 | 12.0 | 768 | 0.5578 | 0.7844 |
| 0.3318 | 13.0 | 832 | 0.4699 | 0.8228 |
| 0.3096 | 14.0 | 896 | 0.4460 | 0.8399 |
| 0.2787 | 15.0 | 960 | 0.4105 | 0.8399 |
| 0.2517 | 16.0 | 1024 | 0.3488 | 0.8578 |
| 0.2346 | 17.0 | 1088 | 0.3877 | 0.8773 |
| 0.2286 | 18.0 | 1152 | 0.3420 | 0.8575 |
| 0.1914 | 19.0 | 1216 | 0.4123 | 0.8682 |
| 0.1844 | 20.0 | 1280 | 0.2894 | 0.8913 |
| 0.173 | 21.0 | 1344 | 0.3197 | 0.8887 |
| 0.1687 | 22.0 | 1408 | 0.2626 | 0.9075 |
| 0.1601 | 23.0 | 1472 | 0.2951 | 0.9068 |
| 0.1466 | 24.0 | 1536 | 0.2666 | 0.9049 |
| 0.1468 | 25.0 | 1600 | 0.2136 | 0.9103 |
| 0.1226 | 26.0 | 1664 | 0.2387 | 0.9127 |
| 0.1186 | 27.0 | 1728 | 0.2131 | 0.9271 |
| 0.0951 | 28.0 | 1792 | 0.2520 | 0.9130 |
| 0.1049 | 29.0 | 1856 | 0.2096 | 0.9259 |
| 0.0936 | 30.0 | 1920 | 0.1875 | 0.9309 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0 |