finalProject – AI Model by amjadfqs | AlphaNeural AI

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amjadfqs/
finalProject

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image-classification

generated_from_trainer

autotrain_compatible

endpoints_compatible

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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