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--- |
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license: apache-2.0 |
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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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: swinv2-tiny-patch4-window8-256-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: 0.9825925925925926 |
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- name: F1 |
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type: f1 |
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value: 0.9825868474705166 |
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- name: Precision |
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type: precision |
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value: 0.9828193476192771 |
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- name: Recall |
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type: recall |
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value: 0.9825925925925926 |
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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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# swinv2-tiny-patch4-window8-256-finetuned-eurosat |
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This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window8-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0510 |
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- Accuracy: 0.9826 |
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- F1: 0.9826 |
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- Precision: 0.9828 |
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- Recall: 0.9826 |
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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: 64 |
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- eval_batch_size: 64 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 256 |
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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: 0.2 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.4479 | 1.0 | 95 | 0.1592 | 0.9478 | 0.9478 | 0.9500 | 0.9478 | |
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| 0.3078 | 2.0 | 190 | 0.0914 | 0.9685 | 0.9686 | 0.9695 | 0.9685 | |
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| 0.2307 | 3.0 | 285 | 0.0603 | 0.9785 | 0.9785 | 0.9790 | 0.9785 | |
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| 0.227 | 4.0 | 380 | 0.0531 | 0.9811 | 0.9811 | 0.9814 | 0.9811 | |
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| 0.1674 | 5.0 | 475 | 0.0510 | 0.9826 | 0.9826 | 0.9828 | 0.9826 | |
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### Framework versions |
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- Transformers 4.22.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.5.1 |
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- Tokenizers 0.12.1 |
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