---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: google-vit-base-patch16-224-in21k-finetuned-food-classification-86M-v0.1
  results: []
---

# food-classification-86M-v0.1

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:
- Loss: 1.6079
- Accuracy: 0.892

## Model description

Food image classification.

## Intended uses & limitations

This was trained for fun and my own learning. But if you want to use it, go ahead.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7263        | 0.99  | 62   | 2.5435          | 0.816    |
| 1.8437        | 2.0   | 125  | 1.7773          | 0.863    |
| 1.5811        | 2.98  | 186  | 1.6079          | 0.892    |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2