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: 0.3404
Accuracy: 0.8770

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	Accuracy
No log	1.0	128	0.8345	0.5996
No log	2.0	256	0.8245	0.6309
No log	3.0	384	0.7492	0.6543
0.8188	4.0	512	0.7173	0.6777
0.8188	5.0	640	0.6625	0.7168
0.8188	6.0	768	0.6182	0.7373
0.8188	7.0	896	0.5058	0.8027
0.5344	8.0	1024	0.5567	0.7764
0.5344	9.0	1152	0.4702	0.8193
0.5344	10.0	1280	0.4502	0.8242
0.5344	11.0	1408	0.4024	0.8408
0.3356	12.0	1536	0.4263	0.8516
0.3356	13.0	1664	0.3782	0.8535
0.3356	14.0	1792	0.3378	0.8604
0.3356	15.0	1920	0.3570	0.8701

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

dewifaj/alzheimer_mri_classification

alzheimer_mri_classification

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

dewifaj/
alzheimer_mri_classification