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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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- image_folder |
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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-cassava3 |
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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: image_folder |
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type: image_folder |
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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: 0.8855140186915887 |
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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-cassava3 |
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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 image_folder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3419 |
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- Accuracy: 0.8855 |
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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: 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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| 0.5624 | 0.99 | 133 | 0.5866 | 0.8166 | |
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| 0.4717 | 1.99 | 266 | 0.4245 | 0.8692 | |
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| 0.4105 | 2.99 | 399 | 0.3708 | 0.8811 | |
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| 0.3753 | 3.99 | 532 | 0.3646 | 0.8787 | |
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| 0.2997 | 4.99 | 665 | 0.3655 | 0.8780 | |
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| 0.3176 | 5.99 | 798 | 0.3545 | 0.8822 | |
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| 0.2849 | 6.99 | 931 | 0.3441 | 0.8850 | |
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| 0.2931 | 7.99 | 1064 | 0.3419 | 0.8855 | |
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| 0.27 | 8.99 | 1197 | 0.3419 | 0.8848 | |
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| 0.2927 | 9.99 | 1330 | 0.3403 | 0.8853 | |
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### Framework versions |
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- Transformers 4.20.1 |
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- Pytorch 1.11.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.12.1 |
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