transformers tensorboard onnx safetensors vit image-classification generated_from_trainer dataset:food101 base_model:google/vit-base-patch16-224-in21k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: food-vit-tutorial
  results:
  - task:
      name: image-classification
      type: image-classification
    dataset:
      name: food101
      type: food101
      config: default
      split: train
      args: default
    metrics:
    - name: accuracy
      type: accuracy
      value: 0.916
datasets:
- food101
---

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# food-vit-tutorial

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0267
- Accuracy: 0.916

## 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7889        | 0.99  | 62   | 2.5577          | 0.838    |
| 1.7142        | 2.0   | 125  | 1.6126          | 0.879    |
| 1.2887        | 2.99  | 187  | 1.2513          | 0.903    |
| 1.0307        | 4.0   | 250  | 1.0673          | 0.922    |
| 1.0022        | 4.96  | 310  | 1.0267          | 0.916    |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0