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README.md
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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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- beans
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metrics:
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- accuracy
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model-index:
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- name: vit-base-beans
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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: beans
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type: beans
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config: default
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split: validation
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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: 0.9774436090225563
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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-beans
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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 beans dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.0863
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- Accuracy: 0.9774
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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: 2e-05
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 1337
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- distributed_type: tpu
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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: 5.0
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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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| 0.2576 | 1.0 | 130 | 0.2310 | 0.9549 |
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| 0.2848 | 2.0 | 260 | 0.1129 | 0.9925 |
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| 0.1504 | 3.0 | 390 | 0.1124 | 0.9624 |
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| 0.0922 | 4.0 | 520 | 0.0860 | 0.9925 |
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| 0.1633 | 5.0 | 650 | 0.0863 | 0.9774 |
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### Framework versions
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- Transformers 4.27.0.dev0
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- Pytorch 2.0.0a0+git45d775c
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- Datasets 2.9.1.dev0
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- Tokenizers 0.12.1
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