---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- renovation
metrics:
- accuracy
model-index:
- name: renovation
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: beans
      type: renovation
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7219562243502052
---

<!-- 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. -->

# renovation

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: 0.6830
- Accuracy: 0.7220

## 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0475        | 0.07  | 100  | 1.0332          | 0.5824   |
| 0.8651        | 0.14  | 200  | 0.9322          | 0.6204   |
| 1.0022        | 0.21  | 300  | 1.2150          | 0.5147   |
| 1.0636        | 0.27  | 400  | 0.9523          | 0.6252   |
| 0.8311        | 0.34  | 500  | 0.8440          | 0.6556   |
| 0.88          | 0.41  | 600  | 0.8707          | 0.6495   |
| 0.8881        | 0.48  | 700  | 0.8903          | 0.6334   |
| 0.7522        | 0.55  | 800  | 0.8479          | 0.6577   |
| 0.798         | 0.62  | 900  | 0.7739          | 0.6843   |
| 0.7317        | 0.68  | 1000 | 0.7856          | 0.6795   |
| 0.8372        | 0.75  | 1100 | 0.8884          | 0.6354   |
| 0.6629        | 0.82  | 1200 | 0.7573          | 0.6871   |
| 0.7767        | 0.89  | 1300 | 0.7543          | 0.6860   |
| 0.9246        | 0.96  | 1400 | 0.7896          | 0.6635   |
| 0.5026        | 1.03  | 1500 | 0.7872          | 0.6813   |
| 0.7599        | 1.1   | 1600 | 0.7861          | 0.6758   |
| 0.5764        | 1.16  | 1700 | 0.8088          | 0.6802   |
| 0.4329        | 1.23  | 1800 | 0.7281          | 0.7059   |
| 0.6271        | 1.3   | 1900 | 0.7291          | 0.7117   |
| 0.5498        | 1.37  | 2000 | 0.7745          | 0.7059   |
| 0.5247        | 1.44  | 2100 | 0.8002          | 0.6891   |
| 0.4891        | 1.51  | 2200 | 0.7014          | 0.7100   |
| 0.5211        | 1.57  | 2300 | 0.7725          | 0.6864   |
| 0.659         | 1.64  | 2400 | 0.7477          | 0.7086   |
| 0.4878        | 1.71  | 2500 | 0.7129          | 0.7052   |
| 0.4941        | 1.78  | 2600 | 0.6830          | 0.7220   |
| 0.4648        | 1.85  | 2700 | 0.7182          | 0.7028   |
| 0.5501        | 1.92  | 2800 | 0.7191          | 0.7144   |
| 0.5491        | 1.98  | 2900 | 0.7132          | 0.7155   |
| 0.2373        | 2.05  | 3000 | 0.7831          | 0.7096   |
| 0.2756        | 2.12  | 3100 | 0.7965          | 0.7247   |
| 0.2299        | 2.19  | 3200 | 0.8241          | 0.7220   |
| 0.2323        | 2.26  | 3300 | 0.8286          | 0.7110   |
| 0.1979        | 2.33  | 3400 | 0.7993          | 0.7302   |
| 0.2507        | 2.4   | 3500 | 0.8477          | 0.7189   |
| 0.205         | 2.46  | 3600 | 0.8197          | 0.7124   |
| 0.35          | 2.53  | 3700 | 0.8348          | 0.7127   |
| 0.3372        | 2.6   | 3800 | 0.8999          | 0.7199   |
| 0.1968        | 2.67  | 3900 | 0.8263          | 0.7274   |
| 0.1443        | 2.74  | 4000 | 0.8704          | 0.7244   |
| 0.1933        | 2.81  | 4100 | 0.8270          | 0.7244   |
| 0.2044        | 2.87  | 4200 | 0.8323          | 0.7274   |
| 0.2709        | 2.94  | 4300 | 0.8494          | 0.7295   |
| 0.1021        | 3.01  | 4400 | 0.8573          | 0.7336   |
| 0.0393        | 3.08  | 4500 | 0.9333          | 0.7377   |
| 0.0973        | 3.15  | 4600 | 0.9646          | 0.7336   |
| 0.0317        | 3.22  | 4700 | 0.9820          | 0.7336   |
| 0.0458        | 3.29  | 4800 | 1.0716          | 0.7326   |
| 0.164         | 3.35  | 4900 | 1.0889          | 0.7312   |
| 0.0578        | 3.42  | 5000 | 1.1011          | 0.7312   |
| 0.0563        | 3.49  | 5100 | 1.1010          | 0.7356   |
| 0.0318        | 3.56  | 5200 | 1.0923          | 0.7343   |
| 0.0255        | 3.63  | 5300 | 1.1156          | 0.7332   |
| 0.0169        | 3.7   | 5400 | 1.1050          | 0.7415   |
| 0.0629        | 3.76  | 5500 | 1.1132          | 0.7373   |
| 0.0627        | 3.83  | 5600 | 1.1110          | 0.7380   |
| 0.0078        | 3.9   | 5700 | 1.1117          | 0.7350   |
| 0.027         | 3.97  | 5800 | 1.1201          | 0.7343   |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2