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

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

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
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microsoft/swin-tiny-patch4-window7-224
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Evaluation results

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