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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-40-aligned_metric-acc-precision-recall-f1 |
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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: train |
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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: 0.8064887989445775 |
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- name: Recall |
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type: recall |
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value: 0.599275070479259 |
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- name: Precision |
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type: precision |
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value: 0.26912642430819317 |
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- name: F1 |
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type: f1 |
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value: 0.37144283574638043 |
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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-40-aligned_metric-acc-precision-recall-f1 |
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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: 0.4464 |
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- Accuracy: 0.8065 |
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- Recall: 0.5993 |
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- Precision: 0.2691 |
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- F1: 0.3714 |
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- Roc Auc: 0.8135 |
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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: 3 |
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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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| 0.1821 | 1.0 | 1348 | 0.1286 | 0.9464 | 0.8533 | 0.8953 | 0.8738 | 0.9858 | |
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| 0.1333 | 2.0 | 2696 | 0.0715 | 0.9725 | 0.9129 | 0.9586 | 0.9352 | 0.9960 | |
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| 0.0809 | 3.0 | 4044 | 0.0520 | 0.9804 | 0.9344 | 0.9743 | 0.9539 | 0.9980 | |
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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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