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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: 0.5026722090261283 |
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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: 1.5983 |
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- Accuracy: 0.5027 |
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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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| No log | 0.89 | 6 | 1.7745 | 0.2838 | |
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| 1.9066 | 1.89 | 12 | 1.7447 | 0.3895 | |
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| 1.9066 | 2.89 | 18 | 1.7216 | 0.4154 | |
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| 1.9875 | 3.89 | 24 | 1.6914 | 0.4311 | |
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| 1.8094 | 4.89 | 30 | 1.6659 | 0.4629 | |
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| 1.8094 | 5.89 | 36 | 1.6485 | 0.4852 | |
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| 1.8906 | 6.89 | 42 | 1.6278 | 0.4869 | |
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| 1.8906 | 7.89 | 48 | 1.6098 | 0.5021 | |
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| 1.8516 | 8.89 | 54 | 1.6131 | 0.5175 | |
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| 1.718 | 9.89 | 60 | 1.5983 | 0.5027 | |
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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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