---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-invitrace-food
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9684
---

<!-- 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-invitrace-food

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: 0.1286
- Accuracy: 0.9684

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6128        | 0.2132 | 100  | 0.4694          | 0.9044   |
| 0.3905        | 0.4264 | 200  | 0.5236          | 0.8484   |
| 0.4315        | 0.6397 | 300  | 0.3988          | 0.8884   |
| 0.4028        | 0.8529 | 400  | 0.2213          | 0.9432   |
| 0.1097        | 1.0661 | 500  | 0.2963          | 0.92     |
| 0.1883        | 1.2793 | 600  | 0.2047          | 0.9448   |
| 0.137         | 1.4925 | 700  | 0.1695          | 0.9548   |
| 0.2309        | 1.7058 | 800  | 0.2159          | 0.9384   |
| 0.094         | 1.9190 | 900  | 0.1987          | 0.9452   |
| 0.0282        | 2.1322 | 1000 | 0.1861          | 0.9528   |
| 0.0231        | 2.3454 | 1100 | 0.1944          | 0.9476   |
| 0.0409        | 2.5586 | 1200 | 0.1625          | 0.96     |
| 0.0386        | 2.7719 | 1300 | 0.1486          | 0.9616   |
| 0.0249        | 2.9851 | 1400 | 0.1736          | 0.9572   |
| 0.012         | 3.1983 | 1500 | 0.1469          | 0.9624   |
| 0.0304        | 3.4115 | 1600 | 0.1405          | 0.9644   |
| 0.0052        | 3.6247 | 1700 | 0.1498          | 0.9636   |
| 0.0247        | 3.8380 | 1800 | 0.1286          | 0.9684   |


### Framework versions

- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1