---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit-finetuned-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: 0.9711538461538461
---

<!-- 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-finetuned-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: 0.1157
- Accuracy: 0.9712

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.193         | 1.0   | 117  | 0.1099          | 0.9808   |
| 0.0462        | 2.0   | 234  | 0.0857          | 0.9808   |
| 0.0171        | 3.0   | 351  | 0.1237          | 0.9712   |
| 0.0123        | 4.0   | 468  | 0.1088          | 0.9712   |
| 0.0095        | 5.0   | 585  | 0.1135          | 0.9712   |
| 0.0081        | 6.0   | 702  | 0.1162          | 0.9712   |
| 0.0073        | 7.0   | 819  | 0.1158          | 0.9712   |
| 0.0066        | 8.0   | 936  | 0.1152          | 0.9712   |
| 0.0061        | 9.0   | 1053 | 0.1160          | 0.9712   |
| 0.0061        | 10.0  | 1170 | 0.1157          | 0.9712   |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3