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---
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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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model-index:
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- name: swin-tiny-patch4-window7-224-ve-U13-b-12
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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: validation
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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: 0.5434782608695652
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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-ve-U13-b-12
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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: 0.9160
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- Accuracy: 0.5435
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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: 12
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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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| No log | 1.0 | 8 | 1.3788 | 0.4348 |
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| 1.3828 | 2.0 | 16 | 1.3084 | 0.5 |
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| 1.2902 | 3.0 | 24 | 1.1908 | 0.4783 |
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| 1.1227 | 4.0 | 32 | 1.1055 | 0.4130 |
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| 0.9806 | 5.0 | 40 | 1.0173 | 0.5217 |
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| 0.9806 | 6.0 | 48 | 0.9396 | 0.5217 |
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| 0.8629 | 7.0 | 56 | 0.9529 | 0.5 |
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| 0.7707 | 8.0 | 64 | 0.9449 | 0.5217 |
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| 0.7411 | 9.0 | 72 | 0.9160 | 0.5435 |
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| 0.671 | 10.0 | 80 | 0.9073 | 0.5435 |
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| 0.671 | 11.0 | 88 | 0.9192 | 0.5435 |
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| 0.6501 | 12.0 | 96 | 0.9456 | 0.5 |
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### Framework versions
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- Transformers 4.36.2
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- Pytorch 2.1.2+cu118
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- Datasets 2.16.1
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- Tokenizers 0.15.0
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