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update model card README.md
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README.md
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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: swinv2-finetuned-eurosat
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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: 0.9827777777777778
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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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# swinv2-finetuned-eurosat
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This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0547
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- Accuracy: 0.9828
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## Model description
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 1
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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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| 0.0555 | 1.0 | 168 | 0.0547 | 0.9828 |
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### Framework versions
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- Transformers 4.
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- Pytorch 1.12.1+cu113
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- Datasets 2.6.1
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- Tokenizers 0.13.1
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- generated_from_trainer
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datasets:
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- imagefolder
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model-index:
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- name: swinv2-finetuned-eurosat
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results: []
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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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# swinv2-finetuned-eurosat
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This model is a fine-tuned version of [microsoft/swinv2-base-patch4-window16-256](https://huggingface.co/microsoft/swinv2-base-patch4-window16-256) on the imagefolder dataset.
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## Model description
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 1
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### Framework versions
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- Transformers 4.24.0
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- Pytorch 1.12.1+cu113
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- Datasets 2.6.1
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- Tokenizers 0.13.1
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