This model is a fine-tuned version of apple/mobilevitv2-1.0-imagenet1k-256 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0046
Accuracy: 0.9988
F1: 0.9988
Recall: 0.9988
Precision: 0.9988

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: 128
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
0.1093	0.2048	128	0.0679	0.9929	0.9929	0.9929	0.9929
0.0234	0.4096	256	0.0170	0.9962	0.9962	0.9962	0.9962
0.0186	0.6144	384	0.0131	0.9973	0.9973	0.9973	0.9973
0.0068	0.8192	512	0.0089	0.9980	0.9981	0.9980	0.9980
0.0049	1.024	640	0.0067	0.9985	0.9985	0.9985	0.9985
0.0113	1.2288	768	0.0064	0.9983	0.9984	0.9983	0.9983
0.0061	1.4336	896	0.0060	0.9983	0.9983	0.9983	0.9984
0.0025	1.6384	1024	0.0058	0.9983	0.9983	0.9983	0.9984
0.0019	1.8432	1152	0.0053	0.9987	0.9986	0.9987	0.9987
0.0056	2.048	1280	0.0051	0.9987	0.9987	0.9987	0.9987
0.0015	2.2528	1408	0.0050	0.9987	0.9987	0.9987	0.9987
0.0055	2.4576	1536	0.0049	0.9988	0.9987	0.9988	0.9988
0.0023	2.6624	1664	0.0049	0.9989	0.9988	0.9989	0.9989
0.0027	2.8672	1792	0.0046	0.9988	0.9988	0.9988	0.9988

Framework versions

Transformers 4.44.2
Pytorch 2.4.1+cu121
Datasets 3.2.0
Tokenizers 0.19.1

nguyenkhoa/mobilevitv2_Liveness_detection_v1.0

mobilevitv2_Liveness_detection_v1.0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nguyenkhoa/
mobilevitv2_Liveness_detection_v1.0