batch-size-16_FFPP-Raw_1FPS_faces-expand-0-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: 0.0442
- Accuracy: 0.9837
- Precision: 0.9831
- Recall: 0.9964
- F1: 0.9897
- Roc Auc: 0.9991
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 |
---|---|---|---|---|---|---|---|---|
0.0483 | 1.0 | 1377 | 0.0442 | 0.9837 | 0.9831 | 0.9964 | 0.9897 | 0.9991 |
Framework versions
- Transformers 4.39.2
- Pytorch 2.3.0
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
- 0
Finetuned from
Evaluation results
- Accuracy on imagefoldertest set self-reported0.984
- Precision on imagefoldertest set self-reported0.983
- Recall on imagefoldertest set self-reported0.996
- F1 on imagefoldertest set self-reported0.990