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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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model-index: |
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- name: resnet-50-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: 0.8239812959251837 |
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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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# resnet-50-finetuned-eurosat |
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This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9095 |
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- Accuracy: 0.8240 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 128 |
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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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| 1.78 | 0.96 | 17 | 1.7432 | 0.4321 | |
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| 1.7105 | 1.96 | 34 | 1.6596 | 0.6307 | |
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| 1.6045 | 2.96 | 51 | 1.5369 | 0.6758 | |
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| 1.6526 | 3.96 | 68 | 1.4111 | 0.7139 | |
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| 1.4018 | 4.96 | 85 | 1.2686 | 0.7602 | |
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| 1.2812 | 5.96 | 102 | 1.1433 | 0.7714 | |
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| 1.3282 | 6.96 | 119 | 1.0643 | 0.7910 | |
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| 1.1246 | 7.96 | 136 | 0.9794 | 0.8133 | |
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| 1.0731 | 8.96 | 153 | 0.9279 | 0.8087 | |
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| 1.0531 | 9.96 | 170 | 0.9095 | 0.8240 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.1 |
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