pytorch tensorboard vit image-classification dataset:beans transformers other-image-classification generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- image-classification
- other-image-classification
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy

model-index:
- name: vit-base-beans-demo
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: beans
      type: beans
      args: default
    metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9774436090225563
---

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# vit-base-beans-demo

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: 0.0853
- Accuracy: 0.9774

## 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: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0545        | 1.54  | 100  | 0.1436          | 0.9624   |
| 0.006         | 3.08  | 200  | 0.1058          | 0.9699   |
| 0.0038        | 4.62  | 300  | 0.0853          | 0.9774   |


### Framework versions

- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
