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--- |
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license: apache-2.0 |
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base_model: moreover18/vit-base-patch16-224-in21k-finetuned-eurosat |
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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-in21k-finetuned-eurosat-finetuned2 |
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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: 0.9261264129915618 |
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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-in21k-finetuned-eurosat-finetuned2 |
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This model is a fine-tuned version of [moreover18/vit-base-patch16-224-in21k-finetuned-eurosat](https://huggingface.co/moreover18/vit-base-patch16-224-in21k-finetuned-eurosat) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1868 |
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- Accuracy: 0.9261 |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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: 3 |
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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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| 0.2258 | 0.25 | 100 | 0.2074 | 0.9155 | |
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| 0.2291 | 0.51 | 200 | 0.2039 | 0.9132 | |
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| 0.212 | 0.76 | 300 | 0.1969 | 0.9147 | |
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| 0.2126 | 1.02 | 400 | 0.2026 | 0.9163 | |
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| 0.1822 | 1.27 | 500 | 0.1952 | 0.9175 | |
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| 0.1716 | 1.53 | 600 | 0.1892 | 0.9225 | |
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| 0.1847 | 1.78 | 700 | 0.1823 | 0.9261 | |
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| 0.1693 | 2.04 | 800 | 0.1879 | 0.9239 | |
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| 0.1438 | 2.29 | 900 | 0.1962 | 0.9206 | |
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| 0.1431 | 2.55 | 1000 | 0.1868 | 0.9261 | |
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| 0.1419 | 2.8 | 1100 | 0.1871 | 0.9252 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 1.12.1+cu116 |
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- Datasets 2.4.0 |
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- Tokenizers 0.15.0 |
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