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--- |
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license: apache-2.0 |
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base_model: WinKawaks/vit-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: vit-tiny-patch16-224-finetuned-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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# vit-tiny-patch16-224-finetuned-RESISC45_01 |
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This model is a fine-tuned version of [WinKawaks/vit-tiny-patch16-224](https://huggingface.co/WinKawaks/vit-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: 0.2402 |
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- Accuracy: 0.9302 |
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- Precision: 0.9317 |
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- Recall: 0.9302 |
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- F1: 0.9301 |
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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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| 3.9864 | 1.0 | 37 | 2.0458 | 0.643 | 0.6573 | 0.643 | 0.6131 | |
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| 0.8947 | 2.0 | 74 | 0.5364 | 0.873 | 0.8821 | 0.873 | 0.8720 | |
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| 0.5981 | 3.0 | 111 | 0.3644 | 0.907 | 0.9137 | 0.907 | 0.9068 | |
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| 0.46 | 4.0 | 148 | 0.2821 | 0.914 | 0.9209 | 0.914 | 0.9130 | |
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| 0.3936 | 5.0 | 185 | 0.2343 | 0.929 | 0.9331 | 0.929 | 0.9289 | |
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| 0.3629 | 6.0 | 222 | 0.2191 | 0.935 | 0.9404 | 0.935 | 0.9351 | |
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| 0.3154 | 7.0 | 259 | 0.2000 | 0.939 | 0.9424 | 0.939 | 0.9388 | |
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| 0.317 | 8.0 | 296 | 0.1736 | 0.952 | 0.9548 | 0.952 | 0.9520 | |
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| 0.2921 | 9.0 | 333 | 0.1725 | 0.952 | 0.9545 | 0.952 | 0.9519 | |
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| 0.2922 | 10.0 | 370 | 0.1738 | 0.945 | 0.9481 | 0.945 | 0.9449 | |
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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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