---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9795191451469278
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# my_awesome_food_model

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1312
- Accuracy: 0.9795

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.9594        | 1.0   | 70   | 3.8779          | 0.6189   |
| 3.0869        | 1.99  | 140  | 3.0415          | 0.8549   |
| 2.471         | 2.99  | 210  | 2.4433          | 0.9270   |
| 2.0406        | 4.0   | 281  | 2.0261          | 0.9501   |
| 1.7238        | 5.0   | 351  | 1.7346          | 0.9581   |
| 1.4513        | 5.99  | 421  | 1.4902          | 0.9671   |
| 1.3131        | 6.99  | 491  | 1.3221          | 0.9786   |
| 1.1752        | 8.0   | 562  | 1.2230          | 0.9768   |
| 1.1007        | 9.0   | 632  | 1.1619          | 0.9795   |
| 1.0682        | 9.96  | 700  | 1.1312          | 0.9795   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3