This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.3183
F1: 0.8946

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: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	F1
No log	1.0	128	0.8686	0.5548
No log	2.0	256	0.8457	0.6087
No log	3.0	384	0.7396	0.6478
0.8172	4.0	512	0.6833	0.6826
0.8172	5.0	640	0.6280	0.7205
0.8172	6.0	768	0.5347	0.7727
0.8172	7.0	896	0.5108	0.7909
0.5292	8.0	1024	0.4707	0.8078
0.5292	9.0	1152	0.4477	0.8302
0.5292	10.0	1280	0.4075	0.8511
0.5292	11.0	1408	0.4263	0.8380
0.3498	12.0	1536	0.3558	0.8756
0.3498	13.0	1664	0.3768	0.8558
0.3498	14.0	1792	0.3412	0.8701
0.3498	15.0	1920	0.3028	0.8952

Framework versions

Transformers 4.41.0
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

dewifaj/alzheimer_classification

alzheimer_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dewifaj/
alzheimer_classification