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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224 |
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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: vit-base-patch16-224-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: test |
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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: 0.8446601941747572 |
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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-base-patch16-224-finetuned-eurosat |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3894 |
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- Accuracy: 0.8447 |
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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: 128 |
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- eval_batch_size: 128 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 512 |
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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: 10 |
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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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| No log | 1.0 | 5 | 1.0761 | 0.5469 | |
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| 1.1435 | 2.0 | 10 | 0.6466 | 0.7735 | |
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| 1.1435 | 3.0 | 15 | 0.4962 | 0.8123 | |
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| 0.5372 | 4.0 | 20 | 0.4365 | 0.8252 | |
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| 0.5372 | 5.0 | 25 | 0.4118 | 0.8382 | |
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| 0.362 | 6.0 | 30 | 0.4031 | 0.8414 | |
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| 0.362 | 7.0 | 35 | 0.3944 | 0.8511 | |
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| 0.3028 | 8.0 | 40 | 0.3930 | 0.8414 | |
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| 0.3028 | 9.0 | 45 | 0.3928 | 0.8479 | |
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| 0.2708 | 10.0 | 50 | 0.3894 | 0.8447 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.14.4 |
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- Tokenizers 0.14.1 |
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