---
license: apache-2.0
tags:
- image-classification
- vision
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-base-beans
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: beans
      type: beans
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9699248120300752
---

<!-- 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-beans

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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1328
- Accuracy: 0.9699

## 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 1337
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0

### Training results

| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.49          | 1.0   | 65   | 0.9624   | 0.4050          |
| 0.2769        | 2.0   | 130  | 0.9850   | 0.1862          |
| 0.1441        | 3.0   | 195  | 0.9774   | 0.1554          |
| 0.1661        | 4.0   | 260  | 0.9774   | 0.1333          |
| 0.1754        | 5.0   | 325  | 0.9699   | 0.1328          |


### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2