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--- |
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license: apache-2.0 |
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base_model: microsoft/swin-tiny-patch4-window7-224 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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- recall |
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- precision |
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- f1 |
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model-index: |
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- name: FFPP-Raw_1FPS_faces-expand-0-aligned |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: test |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.99837772836593 |
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- name: Recall |
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type: recall |
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value: 0.993161411568177 |
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- name: Precision |
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type: precision |
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value: 0.9993696485790828 |
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- name: F1 |
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type: f1 |
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value: 0.9962558584033724 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# FFPP-Raw_1FPS_faces-expand-0-aligned |
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This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0031 |
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- Accuracy: 0.9984 |
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- Recall: 0.9932 |
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- Precision: 0.9994 |
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- F1: 0.9963 |
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- Roc Auc: 1.0000 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | Roc Auc | |
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|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:| |
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| 0.0983 | 1.0 | 1377 | 0.0679 | 0.9743 | 0.9700 | 0.9165 | 0.9425 | 0.9961 | |
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| 0.0917 | 2.0 | 2755 | 0.0342 | 0.9896 | 0.9718 | 0.9803 | 0.9760 | 0.9993 | |
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| 0.0291 | 3.0 | 4132 | 0.0161 | 0.9940 | 0.9908 | 0.9818 | 0.9863 | 0.9998 | |
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| 0.0454 | 4.0 | 5510 | 0.0136 | 0.9950 | 0.9851 | 0.9917 | 0.9884 | 0.9998 | |
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| 0.0302 | 5.0 | 6887 | 0.0075 | 0.9972 | 0.9896 | 0.9976 | 0.9936 | 1.0000 | |
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| 0.0073 | 6.0 | 8265 | 0.0064 | 0.9976 | 0.9931 | 0.9957 | 0.9944 | 1.0000 | |
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| 0.016 | 7.0 | 9642 | 0.0067 | 0.9975 | 0.9934 | 0.9949 | 0.9941 | 1.0000 | |
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| 0.0054 | 8.0 | 11020 | 0.0058 | 0.9978 | 0.9915 | 0.9984 | 0.9949 | 1.0000 | |
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| 0.0237 | 9.0 | 12397 | 0.0063 | 0.9975 | 0.9894 | 0.9993 | 0.9943 | 1.0000 | |
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| 0.0088 | 10.0 | 13775 | 0.0042 | 0.9982 | 0.9920 | 0.9995 | 0.9957 | 1.0000 | |
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| 0.0078 | 11.0 | 15152 | 0.0043 | 0.9982 | 0.9921 | 0.9994 | 0.9957 | 1.0000 | |
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| 0.0142 | 12.0 | 16530 | 0.0040 | 0.9982 | 0.9939 | 0.9979 | 0.9959 | 1.0000 | |
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| 0.0058 | 13.0 | 17907 | 0.0035 | 0.9983 | 0.9930 | 0.9992 | 0.9961 | 1.0000 | |
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| 0.0076 | 14.0 | 19285 | 0.0040 | 0.9981 | 0.9920 | 0.9994 | 0.9957 | 1.0000 | |
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| 0.0032 | 15.0 | 20662 | 0.0036 | 0.9983 | 0.9926 | 0.9995 | 0.9960 | 1.0000 | |
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| 0.0154 | 16.0 | 22040 | 0.0033 | 0.9983 | 0.9928 | 0.9996 | 0.9962 | 1.0000 | |
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| 0.0041 | 17.0 | 23417 | 0.0032 | 0.9984 | 0.9925 | 0.9999 | 0.9962 | 1.0000 | |
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| 0.002 | 18.0 | 24795 | 0.0032 | 0.9984 | 0.9933 | 0.9992 | 0.9962 | 1.0000 | |
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| 0.0024 | 19.0 | 26172 | 0.0031 | 0.9984 | 0.9932 | 0.9994 | 0.9963 | 1.0000 | |
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| 0.0023 | 19.99 | 27540 | 0.0031 | 0.9984 | 0.9927 | 0.9998 | 0.9963 | 1.0000 | |
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### Framework versions |
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- Transformers 4.39.2 |
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- Pytorch 2.2.2 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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