---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-beans-demo-v5
  results: []
---

<!-- 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-demo-v5

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 bact dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0612
- Accuracy: 0.9874

## 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: 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0007        | 0.17  | 100  | 0.1211          | 0.9748   |
| 0.0005        | 0.34  | 200  | 0.1027          | 0.9786   |
| 0.0195        | 0.5   | 300  | 0.0869          | 0.9836   |
| 0.0025        | 0.67  | 400  | 0.0823          | 0.9845   |
| 0.0154        | 0.84  | 500  | 0.0888          | 0.9828   |
| 0.0004        | 1.01  | 600  | 0.0781          | 0.9853   |
| 0.0004        | 1.17  | 700  | 0.0931          | 0.9832   |
| 0.0004        | 1.34  | 800  | 0.0995          | 0.9811   |
| 0.0004        | 1.51  | 900  | 0.0925          | 0.9832   |
| 0.0003        | 1.68  | 1000 | 0.0857          | 0.9836   |
| 0.0364        | 1.85  | 1100 | 0.0788          | 0.9845   |
| 0.0003        | 2.01  | 1200 | 0.0775          | 0.9840   |
| 0.0003        | 2.18  | 1300 | 0.0718          | 0.9857   |
| 0.0003        | 2.35  | 1400 | 0.0804          | 0.9849   |
| 0.0003        | 2.52  | 1500 | 0.0751          | 0.9836   |
| 0.0003        | 2.68  | 1600 | 0.0659          | 0.9870   |
| 0.0002        | 2.85  | 1700 | 0.0612          | 0.9874   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3