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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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- f1 |
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model-index: |
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- name: vit-base-riego |
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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: MaxP--agro_riego |
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split: test |
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args: MaxP--agro_riego |
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metrics: |
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- name: F1 |
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type: f1 |
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value: 0.37288135593220334 |
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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-riego |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2998 |
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- F1: 0.3729 |
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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: 0.0002 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 16 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.1696 | 0.79 | 100 | 1.1385 | 0.352 | |
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| 0.08 | 1.59 | 200 | 0.9071 | 0.3774 | |
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| 0.0928 | 2.38 | 300 | 1.1181 | 0.3454 | |
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| 0.0189 | 3.17 | 400 | 0.8262 | 0.3425 | |
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| 0.0728 | 3.97 | 500 | 0.9647 | 0.3747 | |
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| 0.0756 | 4.76 | 600 | 0.6097 | 0.4776 | |
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| 0.0018 | 5.56 | 700 | 1.3900 | 0.3652 | |
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| 0.002 | 6.35 | 800 | 0.7498 | 0.4606 | |
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| 0.0304 | 7.14 | 900 | 1.4367 | 0.3666 | |
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| 0.0024 | 7.94 | 1000 | 1.5714 | 0.3041 | |
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| 0.0463 | 8.73 | 1100 | 0.8038 | 0.4016 | |
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| 0.0014 | 9.52 | 1200 | 0.7175 | 0.4795 | |
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| 0.0015 | 10.32 | 1300 | 1.0347 | 0.3959 | |
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| 0.0009 | 11.11 | 1400 | 1.3881 | 0.3670 | |
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| 0.0131 | 11.9 | 1500 | 1.0780 | 0.4044 | |
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| 0.0007 | 12.7 | 1600 | 0.9834 | 0.4255 | |
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| 0.0011 | 13.49 | 1700 | 1.0753 | 0.4033 | |
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| 0.0007 | 14.29 | 1800 | 1.1514 | 0.3989 | |
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| 0.0007 | 15.08 | 1900 | 1.2373 | 0.3769 | |
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| 0.0007 | 15.87 | 2000 | 1.2998 | 0.3729 | |
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### Framework versions |
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- Transformers 4.26.1 |
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- Pytorch 1.13.1+cu116 |
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- Datasets 2.10.1 |
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- Tokenizers 0.13.2 |
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