MedicalImages_classification_ViT

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

• Loss: 0.0017
• Accuracy: 1.0
• Precision: 1.0
• Recall: 1.0
• F1: 1.0
• Auc: 1.0

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: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 4
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Auc
0.0796	0.4785	100	0.1065	0.9749	0.9767	0.9749	0.9750	0.9981
0.044	0.9569	200	0.0119	0.9988	0.9988	0.9988	0.9988	1.0000
0.0058	1.4354	300	0.0149	0.9976	0.9976	0.9976	0.9976	0.9999
0.0103	1.9139	400	0.0159	0.9976	0.9976	0.9976	0.9976	1.0000
0.0111	2.3923	500	0.0102	0.9988	0.9988	0.9988	0.9988	1.0000
0.0053	2.8708	600	0.0086	0.9988	0.9988	0.9988	0.9988	1.0000
0.0018	3.3493	700	0.0018	1.0	1.0	1.0	1.0	1.0
0.0017	3.8278	800	0.0017	1.0	1.0	1.0	1.0	1.0

Framework versions

• Transformers 4.41.0.dev0
• Pytorch 2.3.1+cpu
• Datasets 2.20.0
• Tokenizers 0.19.1