batch-size16_FFPP-raw_opencv-1FPS_faces-expand0-aligned_unaugmentation_seed-42_226_2080S_2nd
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: 0.0431
- Accuracy: 0.9839
- Precision: 0.9844
- Recall: 0.9952
- F1: 0.9898
- Roc Auc: 0.9989
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 |
---|---|---|---|---|---|---|---|---|
0.0508 | 0.9996 | 1377 | 0.0431 | 0.9839 | 0.9844 | 0.9952 | 0.9898 | 0.9989 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for hchcsuim/batch-size16_FFPP-raw_opencv-1FPS_faces-expand0-aligned_unaugmentation_seed-42_226_2080S_2nd
Base model
microsoft/swin-tiny-patch4-window7-224
Finetuned
this model
Evaluation results
- Accuracy on imagefoldertest set self-reported0.984
- Precision on imagefoldertest set self-reported0.984
- Recall on imagefoldertest set self-reported0.995
- F1 on imagefoldertest set self-reported0.990