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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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metrics: |
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- accuracy |
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model-index: |
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- name: google-vit-base-patch16-224-in21k-finetuned-food-classification-86M-v0.1 |
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results: [] |
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--- |
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# food-classification-86M-v0.1 |
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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: 1.6079 |
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- Accuracy: 0.892 |
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## Model description |
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Food image classification. |
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## Intended uses & limitations |
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This was trained for fun and my own learning. But if you want to use it, go ahead. |
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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: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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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: 3 |
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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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| 2.7263 | 0.99 | 62 | 2.5435 | 0.816 | |
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| 1.8437 | 2.0 | 125 | 1.7773 | 0.863 | |
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| 1.5811 | 2.98 | 186 | 1.6079 | 0.892 | |
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### Framework versions |
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- Transformers 4.38.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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