| | ---
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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_DFDC_opencv-1FPS_faces-expand10-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: 0.9783404466715985
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| | - name: Precision
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| | type: precision
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| | value: 0.9828799514419597
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| | - name: Recall
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| | type: recall
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| | value: 0.9914814778120139
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| | - name: F1
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| | type: f1
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| | value: 0.9871619778711936
|
| | ---
|
| |
|
| | <!-- 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. -->
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| |
|
| | # batch-size16_DFDC_opencv-1FPS_faces-expand10-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: 0.0559
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| | - Accuracy: 0.9783
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| | - Precision: 0.9829
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| | - Recall: 0.9915
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| | - F1: 0.9872
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| | - Roc Auc: 0.9961
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| |
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| | ## Model description
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| |
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| | More information needed
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| |
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| | ## Intended uses & limitations
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| |
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| | More information needed
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| |
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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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| |
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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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| | | 0.0497 | 1.0000 | 18777 | 0.0559 | 0.9783 | 0.9829 | 0.9915 | 0.9872 | 0.9961 |
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| |
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| |
|
| | ### Framework versions
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| |
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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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| |
|
