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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_keras_callback |
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model-index: |
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- name: food-classifier |
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results: [] |
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datasets: |
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- food101 |
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metrics: |
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- accuracy |
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library_name: transformers |
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--- |
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# food-classifier |
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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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- Train Loss: 0.2136 |
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- Validation Loss: 0.2284 |
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- Train Accuracy: 0.94 |
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- Epoch: 4 |
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## Model description |
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This is an image classification model fine tuned from the Google Vision Transformer (ViT) to classify images of food. |
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## Intended uses & limitations |
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For messing around! |
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## Training and evaluation data |
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The training set contained 101 food classes, over a dataset of 101,000 images. The train/eval split was 80/20 |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} |
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- training_precision: float32 |
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### Training results |
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| Train Loss | Validation Loss | Train Accuracy | Epoch | |
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|:----------:|:---------------:|:--------------:|:-----:| |
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| 0.3409 | 0.2903 | 0.932 | 0 | |
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| 0.2838 | 0.2897 | 0.917 | 1 | |
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| 0.2415 | 0.2869 | 0.914 | 2 | |
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| 0.2143 | 0.2630 | 0.924 | 3 | |
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| 0.2136 | 0.2284 | 0.94 | 4 | |
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### Framework versions |
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- Transformers 4.30.2 |
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- TensorFlow 2.12.0 |
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- Datasets 2.13.1 |
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- Tokenizers 0.13.3 |
