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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-cassava |
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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: 0.8705607476635514 |
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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-cassava |
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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: 0.3742 |
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- Accuracy: 0.8706 |
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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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| 0.5628 | 1.0 | 150 | 0.5357 | 0.8308 | |
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| 0.4398 | 2.0 | 300 | 0.4311 | 0.8598 | |
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| 0.4022 | 3.0 | 450 | 0.3958 | 0.8668 | |
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| 0.3855 | 4.0 | 600 | 0.4030 | 0.8598 | |
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| 0.3659 | 5.0 | 750 | 0.4125 | 0.8617 | |
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| 0.3393 | 6.0 | 900 | 0.3840 | 0.8673 | |
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| 0.3022 | 7.0 | 1050 | 0.3775 | 0.8673 | |
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| 0.2941 | 8.0 | 1200 | 0.3742 | 0.8706 | |
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| 0.2903 | 9.0 | 1350 | 0.3809 | 0.8696 | |
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| 0.2584 | 10.0 | 1500 | 0.3756 | 0.8696 | |
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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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