vit-base-brain-alzheimer-detection

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: 0.2301
• Accuracy: 0.9555

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: 2e-05
• 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
• lr_scheduler_warmup_steps: 500
• num_epochs: 20
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.4285	1.9531	500	0.4633	0.8311
0.171	3.9062	1000	0.3237	0.8994
0.0622	5.8594	1500	0.2032	0.9414
0.0162	7.8125	2000	0.2413	0.9512
0.0044	9.7656	2500	0.1623	0.9668
0.003	11.7188	3000	0.1641	0.9668
0.0025	13.6719	3500	0.1796	0.9619
0.0019	15.625	4000	0.1892	0.9590

Framework versions

• Transformers 4.42.4
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1