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README.md
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---
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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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datasets:
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- food101
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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-food101-24-12
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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: food101
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type: food101
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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: 0.9068514851485149
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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-food101-24-12
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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 food101 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.3533
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- Accuracy: 0.9069
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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: 24
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- eval_batch_size: 24
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 96
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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: 12
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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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| 2.7927 | 1.0 | 789 | 2.5629 | 0.7693 |
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| 1.256 | 2.0 | 1578 | 0.9637 | 0.8583 |
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| 0.94 | 3.0 | 2367 | 0.5866 | 0.8816 |
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| 0.6693 | 4.0 | 3157 | 0.4752 | 0.8888 |
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| 0.6337 | 5.0 | 3946 | 0.4282 | 0.8941 |
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| 0.5811 | 6.0 | 4735 | 0.4110 | 0.8949 |
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| 0.4661 | 7.0 | 5524 | 0.3875 | 0.8990 |
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| 0.4188 | 8.0 | 6314 | 0.3776 | 0.9010 |
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| 0.5045 | 9.0 | 7103 | 0.3633 | 0.9049 |
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| 0.3437 | 10.0 | 7892 | 0.3611 | 0.9058 |
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| 0.3494 | 11.0 | 8681 | 0.3568 | 0.9060 |
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| 0.3381 | 12.0 | 9468 | 0.3533 | 0.9069 |
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### Framework versions
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- Transformers 4.35.0
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- Pytorch 2.1.0+cu118
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- Datasets 2.14.6
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- Tokenizers 0.14.1
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