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

Loss: 0.0031
Accuracy: 0.9984
Recall: 0.9926
Precision: 0.9999
F1: 0.9963
Roc Auc: 1.0000

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: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Recall	Precision	F1	Roc Auc
0.1087	1.0	1377	0.0767	0.9720	0.9020	0.9667	0.9332	0.9965
0.0647	2.0	2755	0.0408	0.9847	0.9458	0.9831	0.9641	0.9990
0.0295	3.0	4132	0.0499	0.9862	0.9947	0.9449	0.9692	0.9995
0.0537	4.0	5510	0.0201	0.9927	0.9736	0.9928	0.9831	0.9998
0.0253	5.0	6887	0.0115	0.9958	0.9826	0.9979	0.9902	0.9999
0.0069	6.0	8265	0.0058	0.9979	0.9928	0.9974	0.9951	1.0000
0.0226	7.0	9642	0.0113	0.9960	0.9823	0.9994	0.9908	0.9999
0.0096	8.0	11020	0.0147	0.9957	0.9806	0.9995	0.9900	0.9999
0.0301	9.0	12397	0.0071	0.9972	0.9878	0.9993	0.9935	1.0000
0.0133	10.0	13775	0.0055	0.9978	0.9901	0.9998	0.9949	1.0000
0.0074	11.0	15152	0.0049	0.9980	0.9915	0.9992	0.9953	1.0000
0.0036	12.0	16530	0.0034	0.9983	0.9935	0.9987	0.9961	1.0000
0.0039	13.0	17907	0.0037	0.9982	0.9926	0.9993	0.9959	1.0000
0.0106	14.0	19285	0.0033	0.9983	0.9931	0.9992	0.9961	1.0000
0.0033	15.0	20662	0.0033	0.9984	0.9926	0.9998	0.9962	1.0000
0.0214	16.0	22040	0.0032	0.9984	0.9931	0.9994	0.9963	1.0000
0.0041	17.0	23417	0.0032	0.9984	0.9926	0.9998	0.9962	1.0000
0.0101	18.0	24795	0.0031	0.9984	0.9926	0.9999	0.9963	1.0000
0.0023	19.0	26172	0.0031	0.9984	0.9928	0.9997	0.9963	1.0000
0.002	19.99	27540	0.0031	0.9984	0.9926	0.9999	0.9963	1.0000

Framework versions

Transformers 4.39.2
Pytorch 2.2.2
Datasets 2.18.0
Tokenizers 0.15.2

hchcsuim/FFPP-Raw_1FPS_faces-expand-0-aligned-normalize-image-mean-std

FFPP-Raw_1FPS_faces-expand-0-aligned-normalize-image-mean-std

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

hchcsuim/
FFPP-Raw_1FPS_faces-expand-0-aligned-normalize-image-mean-std