swin-tiny-patch4-window7-224-finetuned-FaceAIorNot-105330
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: 0.0233
- Accuracy: 0.9935
- Precision: 0.9925
- Recall: 0.9947
- F1: 0.9936
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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
---|---|---|---|---|---|---|---|
0.0862 | 1.0 | 740 | 0.0694 | 0.9740 | 0.9731 | 0.9756 | 0.9743 |
0.0914 | 2.0 | 1481 | 0.0396 | 0.9862 | 0.9814 | 0.9916 | 0.9865 |
0.0184 | 3.0 | 2222 | 0.0784 | 0.9777 | 0.9618 | 0.9955 | 0.9783 |
0.0181 | 4.0 | 2963 | 0.0330 | 0.9907 | 0.9879 | 0.9938 | 0.9908 |
0.03 | 4.99 | 3700 | 0.0233 | 0.9935 | 0.9925 | 0.9947 | 0.9936 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.1.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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Evaluation results
- Accuracy on imagefolderself-reported0.994
- Precision on imagefolderself-reported0.993
- Recall on imagefolderself-reported0.995
- F1 on imagefolderself-reported0.994