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README.md
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---
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license: apache-2.0
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base_model: facebook/deit-tiny-patch16-224
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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- precision
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- recall
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- f1
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model-index:
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- name: deit-tiny-patch16-224-RESISC45_01
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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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should probably proofread and complete it, then remove this comment. -->
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# deit-tiny-patch16-224-RESISC45_01
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This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3266
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- Accuracy: 0.912
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- Precision: 0.9184
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- Recall: 0.912
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- F1: 0.9127
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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: 0.0001
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- train_batch_size: 512
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- eval_batch_size: 512
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- seed: 42
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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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- num_epochs: 10
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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| 2.1588 | 1.0 | 37 | 1.4843 | 0.716 | 0.7429 | 0.716 | 0.7079 |
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| 1.1043 | 2.0 | 74 | 0.8240 | 0.825 | 0.8391 | 0.825 | 0.8245 |
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| 0.801 | 3.0 | 111 | 0.5870 | 0.866 | 0.8733 | 0.866 | 0.8660 |
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| 0.6546 | 4.0 | 148 | 0.4760 | 0.885 | 0.8916 | 0.885 | 0.8852 |
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| 0.5632 | 5.0 | 185 | 0.4202 | 0.896 | 0.9038 | 0.896 | 0.8963 |
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| 0.5004 | 6.0 | 222 | 0.3792 | 0.895 | 0.9046 | 0.895 | 0.8953 |
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| 0.4392 | 7.0 | 259 | 0.3483 | 0.906 | 0.9126 | 0.906 | 0.9067 |
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| 0.4358 | 8.0 | 296 | 0.3436 | 0.907 | 0.9150 | 0.907 | 0.9084 |
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| 0.4208 | 9.0 | 333 | 0.3298 | 0.908 | 0.9135 | 0.908 | 0.9086 |
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| 0.4148 | 10.0 | 370 | 0.3266 | 0.912 | 0.9184 | 0.912 | 0.9127 |
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### Framework versions
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- Transformers 4.44.0
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- Pytorch 2.4.0
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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