| ---
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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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| - precision
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| - recall
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| - f1
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| model-index:
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| - name: batch-size16_Celeb-DF_opencv-1FPS_faces-expand40-aligned_unaugmentation
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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: 0.9438615721891638
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| - name: Precision
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| type: precision
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| value: 0.9452811692362535
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| - name: Recall
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| type: recall
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| value: 0.9903828197945845
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| - name: F1
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| type: f1
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| value: 0.967306552368793
|
| ---
|
|
|
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| should probably proofread and complete it, then remove this comment. -->
|
|
|
| # batch-size16_Celeb-DF_opencv-1FPS_faces-expand40-aligned_unaugmentation
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|
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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: 0.1504
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| - Accuracy: 0.9439
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| - Precision: 0.9453
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| - Recall: 0.9904
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| - F1: 0.9673
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| - Roc Auc: 0.9728
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|
|
| ## Model description
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|
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| More information needed
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|
|
| ## Intended uses & limitations
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|
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| More information needed
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|
|
| ## Training and evaluation data
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|
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| More information needed
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|
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| ## Training procedure
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|
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| ### Training hyperparameters
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|
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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: 1
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|
|
| ### Training results
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|
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| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
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| |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
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| | 0.2003 | 0.9962 | 199 | 0.1504 | 0.9439 | 0.9453 | 0.9904 | 0.9673 | 0.9728 |
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|
|
|
|
| ### Framework versions
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|
|
| - Transformers 4.41.2
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| - Pytorch 2.3.1
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| - Datasets 2.20.0
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| - Tokenizers 0.19.1
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|
|
