README.md

metadata

license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
  - generated_from_trainer
datasets:
  - imagefolder
metrics:
  - accuracy
  - recall
  - precision
  - f1
model-index:
  - name: FFPP-Raw_1FPS_faces-expand-0-aligned_augmentation-normalize-image-mean-std
    results:
      - task:
          name: Image Classification
          type: image-classification
        dataset:
          name: imagefolder
          type: imagefolder
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.998321005581522
          - name: Recall
            type: recall
            value: 0.9929003967425349
          - name: Precision
            type: precision
            value: 0.9993694829760403
          - name: F1
            type: f1
            value: 0.9961244369959149

FFPP-Raw_1FPS_faces-expand-0-aligned_augmentation-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: 0.0034
• Accuracy: 0.9983
• Recall: 0.9929
• Precision: 0.9994
• F1: 0.9961
• 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
0.1054	1.0	1377	0.0750	0.9716	0.9180	0.9495	0.9335	0.9957
0.0785	2.0	2755	0.0406	0.9853	0.9596	0.9723	0.9660	0.9986
0.0713	3.0	4132	0.0348	0.9878	0.9534	0.9899	0.9713	0.9994
0.0447	4.0	5510	0.0172	0.9933	0.9842	0.9851	0.9846	0.9997
0.0388	5.0	6887	0.0186	0.9936	0.9741	0.9964	0.9851	0.9998
0.0236	6.0	8265	0.0119	0.9957	0.9830	0.9971	0.9900	0.9999
0.031	7.0	9642	0.0137	0.9957	0.9928	0.9873	0.9900	0.9999
0.015	8.0	11020	0.0072	0.9972	0.9903	0.9969	0.9936	1.0000
0.0429	9.0	12397	0.0087	0.9967	0.9863	0.9987	0.9925	0.9999
0.0186	10.0	13775	0.0052	0.9979	0.9919	0.9985	0.9952	1.0000
0.0282	11.0	15152	0.0069	0.9974	0.9892	0.9988	0.9940	1.0000
0.0034	12.0	16530	0.0045	0.9979	0.9947	0.9956	0.9951	1.0000
0.0187	13.0	17907	0.0070	0.9972	0.9886	0.9986	0.9935	1.0000
0.0136	14.0	19285	0.0038	0.9982	0.9931	0.9988	0.9959	1.0000
0.006	15.0	20662	0.0039	0.9982	0.9928	0.9988	0.9958	1.0000
0.0067	16.0	22040	0.0037	0.9983	0.9926	0.9995	0.9960	1.0000
0.0121	17.0	23417	0.0036	0.9983	0.9929	0.9992	0.9960	1.0000
0.0026	18.0	24795	0.0037	0.9982	0.9925	0.9993	0.9959	1.0000
0.0024	19.0	26172	0.0034	0.9983	0.9932	0.9991	0.9961	1.0000
0.002	19.99	27540	0.0034	0.9983	0.9929	0.9994	0.9961	1.0000

Framework versions

• Transformers 4.39.2
• Pytorch 2.2.2
• Datasets 2.18.0
• Tokenizers 0.15.2