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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-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: 0.796756082345602
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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-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 imagefolder dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.6838
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- Accuracy: 0.7968
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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: 15
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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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| 1.5891 | 0.9966 | 218 | 1.3833 | 0.5723 |
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| 1.2997 | 1.9977 | 437 | 1.0831 | 0.6700 |
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| 1.1166 | 2.9989 | 656 | 0.9937 | 0.6958 |
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| 1.0464 | 4.0 | 875 | 0.9180 | 0.7231 |
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| 0.982 | 4.9966 | 1093 | 0.8399 | 0.7432 |
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| 0.9472 | 5.9977 | 1312 | 0.8127 | 0.7536 |
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| 0.8751 | 6.9989 | 1531 | 0.7852 | 0.7639 |
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| 0.9107 | 8.0 | 1750 | 0.7644 | 0.7713 |
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| 0.8464 | 8.9966 | 1968 | 0.7322 | 0.7830 |
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| 0.8398 | 9.9977 | 2187 | 0.7243 | 0.7798 |
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| 0.7534 | 10.9989 | 2406 | 0.7088 | 0.7845 |
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| 0.7051 | 12.0 | 2625 | 0.6982 | 0.7935 |
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| 0.7359 | 12.9966 | 2843 | 0.6985 | 0.7916 |
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| 0.7641 | 13.9977 | 3062 | 0.6838 | 0.7968 |
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| 0.7372 | 14.9486 | 3270 | 0.6781 | 0.7968 |
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### Framework versions
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- Transformers 4.41.2
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- Pytorch 2.3.1+cu121
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- Datasets 2.19.1
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- Tokenizers 0.19.1
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