batch-size-16_FFPP-c23_1FPS_faces-expand-10-aligned_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: 0.1604
• Accuracy: 0.9332
• Precision: 0.9375
• Recall: 0.9800
• F1: 0.9583
• Roc Auc: 0.9796

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
0.1915	1.0	1381	0.1604	0.9332	0.9375	0.9800	0.9583	0.9796

Framework versions

• Transformers 4.39.2
• Pytorch 2.3.0
• Datasets 2.18.0
• Tokenizers 0.15.2