batch-size16_FFPP-c23_ffmpeg-1FPS-qv1_unaugmentation
This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.2853
- Accuracy: 0.8746
- Precision: 0.8733
- Recall: 0.9822
- F1: 0.9245
- Roc Auc: 0.9279
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
---|---|---|---|---|---|---|---|---|
0.3162 | 1.0 | 1344 | 0.2853 | 0.8746 | 0.8733 | 0.9822 | 0.9245 | 0.9279 |
Framework versions
- Transformers 4.39.2
- Pytorch 2.3.0
- Datasets 2.18.0
- Tokenizers 0.15.2
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Finetuned from
Evaluation results
- Accuracy on imagefoldertest set self-reported0.875
- Precision on imagefoldertest set self-reported0.873
- Recall on imagefoldertest set self-reported0.982
- F1 on imagefoldertest set self-reported0.925