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README.md
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---
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license: apache-2.0
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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-finetuned-omars6
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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: 0.8814589665653495
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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-finetuned-omars6
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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: 0.5625
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- Accuracy: 0.8815
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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: 0.0005
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 32
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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: 30
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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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| 0.9598 | 0.99 | 92 | 0.7744 | 0.6869 |
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| 0.7825 | 2.0 | 185 | 0.7336 | 0.7082 |
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| 0.9638 | 2.99 | 277 | 0.8202 | 0.7204 |
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| 1.0288 | 4.0 | 370 | 0.8621 | 0.7903 |
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| 0.9711 | 4.99 | 462 | 0.8212 | 0.6809 |
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| 1.0125 | 6.0 | 555 | 0.8700 | 0.7356 |
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| 0.945 | 6.99 | 647 | 0.7959 | 0.7781 |
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| 0.9851 | 8.0 | 740 | 0.8755 | 0.6140 |
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| 0.8078 | 8.99 | 832 | 0.6970 | 0.7781 |
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| 0.7377 | 10.0 | 925 | 0.6063 | 0.7386 |
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| 0.7934 | 10.99 | 1017 | 0.6121 | 0.8116 |
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| 0.7986 | 12.0 | 1110 | 0.6532 | 0.8116 |
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| 0.6129 | 12.99 | 1202 | 0.7250 | 0.8450 |
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| 0.7428 | 14.0 | 1295 | 0.6417 | 0.7264 |
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| 0.5661 | 14.99 | 1387 | 0.6847 | 0.7964 |
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| 0.6631 | 16.0 | 1480 | 0.5470 | 0.8298 |
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| 0.5787 | 16.99 | 1572 | 0.5696 | 0.8359 |
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| 0.6635 | 18.0 | 1665 | 0.6385 | 0.7872 |
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| 0.5251 | 18.99 | 1757 | 0.5842 | 0.8419 |
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| 0.6164 | 20.0 | 1850 | 0.5506 | 0.8207 |
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| 0.4166 | 20.99 | 1942 | 0.8169 | 0.8055 |
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| 0.4189 | 22.0 | 2035 | 0.5882 | 0.8480 |
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| 0.699 | 22.99 | 2127 | 0.5767 | 0.8541 |
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| 0.6095 | 24.0 | 2220 | 0.6392 | 0.8845 |
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| 0.3837 | 24.99 | 2312 | 0.6109 | 0.8723 |
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| 0.4916 | 26.0 | 2405 | 0.4862 | 0.8754 |
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| 0.4536 | 26.99 | 2497 | 0.5625 | 0.8754 |
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| 0.3636 | 28.0 | 2590 | 0.5948 | 0.8663 |
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| 0.4004 | 28.99 | 2682 | 0.5735 | 0.8906 |
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| 0.4248 | 29.84 | 2760 | 0.5625 | 0.8815 |
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### Framework versions
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- Transformers 4.30.2
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- Pytorch 2.0.1+cu117
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- Datasets 2.13.0
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- Tokenizers 0.13.3
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