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README.md
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---
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library_name: transformers
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license: apache-2.0
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base_model: google/vit-base-patch16-224-in21k
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: vit-cifar100-cifar100
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results: []
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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-cifar100-cifar100
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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 an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.2955
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- Accuracy: 0.9241
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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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- 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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| 1.4469 | 1.0 | 5313 | 1.1871 | 0.8716 |
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| 0.7861 | 2.0 | 10626 | 0.4685 | 0.9056 |
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| 0.732 | 3.0 | 15939 | 0.3551 | 0.9139 |
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| 0.3327 | 4.0 | 21252 | 0.3090 | 0.9199 |
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| 0.4886 | 5.0 | 26565 | 0.2955 | 0.9241 |
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### Framework versions
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- Transformers 4.44.2
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- Pytorch 2.0.1+cu117
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- Datasets 3.0.0
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- Tokenizers 0.19.1
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