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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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- precision
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- recall
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- f1
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model-index:
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- name: batch-size16_FFPP-c23_ffmpeg-1FPS-qv1_faces-expand0-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: 0.9341693883006543
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- name: Precision
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type: precision
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value: 0.9457024697784718
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- name: Recall
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type: recall
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value: 0.9716386866735998
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- name: F1
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type: f1
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value: 0.9584951563026688
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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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# batch-size16_FFPP-c23_ffmpeg-1FPS-qv1_faces-expand0-aligned_unaugmentation
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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: 0.1590
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- Accuracy: 0.9342
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- Precision: 0.9457
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- Recall: 0.9716
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- F1: 0.9585
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- Roc Auc: 0.9778
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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: 1
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
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| 0.2014 | 1.0 | 1344 | 0.1590 | 0.9342 | 0.9457 | 0.9716 | 0.9585 | 0.9778 |
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### Framework versions
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- Transformers 4.39.2
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- Pytorch 2.3.0
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- Datasets 2.18.0
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- Tokenizers 0.15.2
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