---
datasets:
- food101
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: vit-base-patch16-224-in21k-finetuned-lora-food101
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: food101
      type: food101
      config: default
      split: train[:5000]
      args: default
    metrics:
    - type: accuracy
      value: 0.96
      name: Accuracy
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-patch16-224-in21k-finetuned-lora-food101

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: 0.1448
- Accuracy: 0.96

## 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: 0.005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 9    | 0.5069          | 0.896    |
| 2.1627        | 2.0   | 18   | 0.1891          | 0.946    |
| 0.3451        | 3.0   | 27   | 0.1448          | 0.96     |
| 0.2116        | 4.0   | 36   | 0.1509          | 0.958    |
| 0.1711        | 5.0   | 45   | 0.1498          | 0.958    |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2