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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: microsoft/swin-tiny-patch4-window7-224 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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- precision |
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- recall |
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- f1 |
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model-index: |
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- name: bridalMakeupClassifier_binary |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 1.0 |
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- name: Precision |
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type: precision |
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value: 1.0 |
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- name: Recall |
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type: recall |
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value: 1.0 |
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- name: F1 |
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type: f1 |
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value: 1.0 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bridalMakeupClassifier_binary |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0072 |
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- Accuracy: 1.0 |
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- Precision: 1.0 |
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- Recall: 1.0 |
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- F1: 1.0 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 20 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| |
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| 0.2966 | 1.0 | 23 | 0.1290 | 0.9662 | 0.9432 | 0.9326 | 0.9379 | |
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| 0.1233 | 2.0 | 46 | 0.0407 | 0.9877 | 0.9670 | 0.9888 | 0.9778 | |
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| 0.0469 | 3.0 | 69 | 0.0594 | 0.9815 | 0.9368 | 1.0 | 0.9674 | |
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| 0.0394 | 4.0 | 92 | 0.0557 | 0.9877 | 0.9670 | 0.9888 | 0.9778 | |
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| 0.0909 | 5.0 | 115 | 0.0401 | 0.9908 | 0.9674 | 1.0 | 0.9834 | |
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| 0.05 | 6.0 | 138 | 0.0252 | 0.9877 | 0.9670 | 0.9888 | 0.9778 | |
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| 0.0451 | 7.0 | 161 | 0.0279 | 0.9877 | 0.9885 | 0.9663 | 0.9773 | |
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| 0.0231 | 8.0 | 184 | 0.0278 | 0.9938 | 0.9780 | 1.0 | 0.9889 | |
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| 0.0404 | 9.0 | 207 | 0.0256 | 0.9877 | 0.9775 | 0.9775 | 0.9775 | |
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| 0.0297 | 10.0 | 230 | 0.0260 | 0.9908 | 0.9778 | 0.9888 | 0.9832 | |
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| 0.0327 | 11.0 | 253 | 0.0230 | 0.9938 | 0.9780 | 1.0 | 0.9889 | |
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| 0.0221 | 12.0 | 276 | 0.0140 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
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| 0.0294 | 13.0 | 299 | 0.0106 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
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| 0.0292 | 14.0 | 322 | 0.0132 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
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| 0.0064 | 15.0 | 345 | 0.0231 | 0.9908 | 0.9674 | 1.0 | 0.9834 | |
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| 0.02 | 16.0 | 368 | 0.0087 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
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| 0.0356 | 17.0 | 391 | 0.0114 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
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| 0.0232 | 18.0 | 414 | 0.0072 | 1.0 | 1.0 | 1.0 | 1.0 | |
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| 0.0351 | 19.0 | 437 | 0.0087 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
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| 0.0155 | 20.0 | 460 | 0.0075 | 0.9969 | 0.9889 | 1.0 | 0.9944 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.20.0 |
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