---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: food-classifier
  results: []
datasets:
- food101
metrics:
- accuracy
library_name: transformers
---

# food-classifier

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.
It achieves the following results on the evaluation set:
- Train Loss: 0.2136
- Validation Loss: 0.2284
- Train Accuracy: 0.94
- Epoch: 4

## Model description

This is an image classification model fine tuned from the Google Vision Transformer (ViT) to classify images of food. 

## Intended uses & limitations

For messing around!

## Training and evaluation data

The training set contained 101 food classes, over a dataset of 101,000 images. The train/eval split was 80/20

### Training hyperparameters

The following hyperparameters were used during training:
- 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}
- training_precision: float32

### Training results

| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.3409     | 0.2903          | 0.932          | 0     |
| 0.2838     | 0.2897          | 0.917          | 1     |
| 0.2415     | 0.2869          | 0.914          | 2     |
| 0.2143     | 0.2630          | 0.924          | 3     |
| 0.2136     | 0.2284          | 0.94           | 4     |


### Framework versions

- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3