---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
base_model: google/vit-base-patch16-224-in21k
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
      args: default
    metrics:
    - type: accuracy
      value: 0.855973597359736
      name: Accuracy
---

<!-- 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. -->

# 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: 0.5152
- Accuracy: 0.8560

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8353        | 1.0   | 133  | 0.6692          | 0.8168   |
| 0.702         | 2.0   | 266  | 0.5892          | 0.8393   |
| 0.6419        | 2.99  | 399  | 0.5615          | 0.8455   |
| 0.5742        | 4.0   | 533  | 0.5297          | 0.8535   |
| 0.4942        | 4.99  | 665  | 0.5152          | 0.8560   |


### Framework versions

- PEFT 0.5.0.dev0
- Transformers 4.32.0.dev0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3