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--- |
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license: apache-2.0 |
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base_model: 100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification |
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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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model-index: |
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- name: swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon |
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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: 0.9930313588850174 |
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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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# swin-tiny-patch4-window7-224-spa_saloon_classification-spa-saloon |
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This model is a fine-tuned version of [100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification](https://huggingface.co./100rab25/swin-tiny-patch4-window7-224-spa_saloon_classification) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0408 |
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- Accuracy: 0.9930 |
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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: 10 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.2637 | 0.99 | 20 | 0.1274 | 0.9443 | |
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| 0.2582 | 1.98 | 40 | 0.0937 | 0.9756 | |
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| 0.161 | 2.96 | 60 | 0.0924 | 0.9582 | |
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| 0.1535 | 4.0 | 81 | 0.0612 | 0.9861 | |
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| 0.1347 | 4.99 | 101 | 0.0536 | 0.9791 | |
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| 0.1155 | 5.98 | 121 | 0.0408 | 0.9930 | |
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| 0.1306 | 6.96 | 141 | 0.0417 | 0.9930 | |
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| 0.1017 | 8.0 | 162 | 0.0380 | 0.9895 | |
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| 0.0859 | 8.99 | 182 | 0.0417 | 0.9895 | |
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| 0.0897 | 9.88 | 200 | 0.0393 | 0.9895 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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