| --- |
| library_name: transformers |
| license: apache-2.0 |
| base_model: microsoft/swin-tiny-patch4-window7-224 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| - precision |
| - recall |
| - f1 |
| model-index: |
| - name: bridalMakeupClassifier_binary |
| results: |
| - task: |
| name: Image Classification |
| type: image-classification |
| dataset: |
| name: imagefolder |
| type: imagefolder |
| config: default |
| split: train |
| args: default |
| metrics: |
| - name: Accuracy |
| type: accuracy |
| value: 1.0 |
| - name: Precision |
| type: precision |
| value: 1.0 |
| - name: Recall |
| type: recall |
| value: 1.0 |
| - name: F1 |
| type: f1 |
| value: 1.0 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # bridalMakeupClassifier_binary |
| |
| This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0072 |
| - Accuracy: 1.0 |
| - Precision: 1.0 |
| - Recall: 1.0 |
| - F1: 1.0 |
| |
| ## 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: 20 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
| | 0.2966 | 1.0 | 23 | 0.1290 | 0.9662 | 0.9432 | 0.9326 | 0.9379 | |
| | 0.1233 | 2.0 | 46 | 0.0407 | 0.9877 | 0.9670 | 0.9888 | 0.9778 | |
| | 0.0469 | 3.0 | 69 | 0.0594 | 0.9815 | 0.9368 | 1.0 | 0.9674 | |
| | 0.0394 | 4.0 | 92 | 0.0557 | 0.9877 | 0.9670 | 0.9888 | 0.9778 | |
| | 0.0909 | 5.0 | 115 | 0.0401 | 0.9908 | 0.9674 | 1.0 | 0.9834 | |
| | 0.05 | 6.0 | 138 | 0.0252 | 0.9877 | 0.9670 | 0.9888 | 0.9778 | |
| | 0.0451 | 7.0 | 161 | 0.0279 | 0.9877 | 0.9885 | 0.9663 | 0.9773 | |
| | 0.0231 | 8.0 | 184 | 0.0278 | 0.9938 | 0.9780 | 1.0 | 0.9889 | |
| | 0.0404 | 9.0 | 207 | 0.0256 | 0.9877 | 0.9775 | 0.9775 | 0.9775 | |
| | 0.0297 | 10.0 | 230 | 0.0260 | 0.9908 | 0.9778 | 0.9888 | 0.9832 | |
| | 0.0327 | 11.0 | 253 | 0.0230 | 0.9938 | 0.9780 | 1.0 | 0.9889 | |
| | 0.0221 | 12.0 | 276 | 0.0140 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
| | 0.0294 | 13.0 | 299 | 0.0106 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
| | 0.0292 | 14.0 | 322 | 0.0132 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
| | 0.0064 | 15.0 | 345 | 0.0231 | 0.9908 | 0.9674 | 1.0 | 0.9834 | |
| | 0.02 | 16.0 | 368 | 0.0087 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
| | 0.0356 | 17.0 | 391 | 0.0114 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
| | 0.0232 | 18.0 | 414 | 0.0072 | 1.0 | 1.0 | 1.0 | 1.0 | |
| | 0.0351 | 19.0 | 437 | 0.0087 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
| | 0.0155 | 20.0 | 460 | 0.0075 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.45.1 |
| - Pytorch 2.4.1+cu121 |
| - Datasets 2.21.0 |
| - Tokenizers 0.20.0 |
| |
