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README.md
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@@ -4,7 +4,7 @@ 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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metrics:
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- accuracy
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model-index:
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name: Image Classification
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type: image-classification
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dataset:
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name:
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type:
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config:
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split: train
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args:
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metrics:
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- name: Accuracy
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type: accuracy
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value: 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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# swin-tiny-patch4-window7-224-finetuned-eurosat
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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
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It achieves the following results on the evaluation set:
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- Loss: 0.
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- Accuracy: 0.
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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### Framework versions
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- Transformers 4.33.
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- Pytorch 2.0.
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- Datasets 2.
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- Tokenizers 0.13.3
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tags:
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- generated_from_trainer
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datasets:
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- cifar10
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metrics:
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- accuracy
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model-index:
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name: Image Classification
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type: image-classification
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dataset:
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name: cifar10
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type: cifar10
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config: plain_text
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split: train
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args: plain_text
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.973
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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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# swin-tiny-patch4-window7-224-finetuned-eurosat
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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 cifar10 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0799
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- Accuracy: 0.973
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## Model description
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| 0.5238 | 1.0 | 351 | 0.1417 | 0.9512 |
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| 0.3708 | 2.0 | 703 | 0.0975 | 0.9688 |
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| 0.3201 | 2.99 | 1053 | 0.0799 | 0.973 |
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### Framework versions
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- Transformers 4.33.0
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- Pytorch 2.0.0
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- Datasets 2.1.0
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- Tokenizers 0.13.3
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