
<!-- 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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0292
- Accuracy: 0.9926

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

              precision    recall  f1-score   support

           0       1.00      1.00      1.00     11315
           1       0.92      0.94      0.93       204
           2       0.95      0.97      0.96       714
           3       0.87      0.98      0.92       171
   macro avg       0.93      0.97      0.95     12404
weighted avg       0.99      0.99      0.99     12404
    accuracy                           0.99     12404



## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0206        | 1.72  | 1000 | 0.0422          | 0.9854   |
| 0.0008        | 3.44  | 2000 | 0.0316          | 0.9918   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2

no building or railspace railspace building railspace and non railspace building