finalProject

This model is a fine-tuned version of microsoft/swin-base-patch4-window7-224-in22k on the imagefolder dataset. It achieves the following results on the evaluation set:

• Loss: 0.0411
• Accuracy: 0.9890
• F1 Score: 0.9892
• Precision: 0.9894
• Sensitivity: 0.9891
• Specificity: 0.9972

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: 0.0001
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1 Score	Precision	Sensitivity	Specificity
0.3384	1.0	30	0.2387	0.9144	0.9163	0.9197	0.9146	0.9781
0.1608	2.0	60	0.1635	0.9466	0.9476	0.9485	0.9474	0.9865
0.0953	3.0	90	0.0915	0.9698	0.9703	0.9706	0.9706	0.9924
0.0573	4.0	120	0.1125	0.9607	0.9617	0.9634	0.9621	0.9901
0.0335	5.0	150	0.0536	0.9827	0.9831	0.9837	0.9826	0.9957
0.0185	6.0	180	0.0543	0.9827	0.9830	0.9837	0.9825	0.9957
0.0226	7.0	210	0.0478	0.9859	0.9861	0.9866	0.9856	0.9965
0.0131	8.0	240	0.0468	0.9843	0.9846	0.9847	0.9846	0.9961
0.0087	9.0	270	0.0411	0.9890	0.9892	0.9894	0.9891	0.9972
0.0043	10.0	300	0.0376	0.9886	0.9888	0.9890	0.9887	0.9971

Framework versions

• Transformers 4.30.2
• Pytorch 2.0.1+cu117
• Datasets 2.13.0
• Tokenizers 0.13.3