---
license: apache-2.0
tags:
- 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: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9849624060150376
---

<!-- 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:
- Accuracy: 0.9850
- Loss: 0.0711

## 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: 8
- eval_batch_size: 8
- 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 |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 0.3303        | 1.0   | 130  | 0.9624   | 0.2543          |
| 0.1519        | 2.0   | 260  | 0.9850   | 0.1059          |
| 0.1879        | 3.0   | 390  | 0.9850   | 0.0908          |
| 0.1189        | 4.0   | 520  | 0.9850   | 0.0714          |
| 0.1095        | 5.0   | 650  | 0.9850   | 0.0711          |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1