---
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: vit-base-renovation2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: renovations
      type: renovation
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6666666666666666
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# vit-base-renovation2

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 renovations dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8273
- Accuracy: 0.6667

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.359         | 0.2   | 25   | 1.2074          | 0.4658   |
| 1.1384        | 0.4   | 50   | 1.1213          | 0.5205   |
| 1.0866        | 0.6   | 75   | 0.9746          | 0.6301   |
| 1.1787        | 0.81  | 100  | 1.0523          | 0.5662   |
| 0.9242        | 1.01  | 125  | 0.9543          | 0.6256   |
| 0.7945        | 1.21  | 150  | 0.9200          | 0.6119   |
| 0.8379        | 1.41  | 175  | 0.8447          | 0.6712   |
| 0.7253        | 1.61  | 200  | 0.8642          | 0.6575   |
| 0.6344        | 1.81  | 225  | 0.8443          | 0.6438   |
| 0.6521        | 2.02  | 250  | 0.8273          | 0.6667   |
| 0.3627        | 2.22  | 275  | 0.8653          | 0.6712   |
| 0.2523        | 2.42  | 300  | 0.8748          | 0.6895   |
| 0.363         | 2.62  | 325  | 0.8407          | 0.6849   |
| 0.3433        | 2.82  | 350  | 0.9696          | 0.6484   |
| 0.2874        | 3.02  | 375  | 0.9290          | 0.6804   |
| 0.1682        | 3.23  | 400  | 0.9713          | 0.6575   |
| 0.1575        | 3.43  | 425  | 0.9963          | 0.6804   |
| 0.0822        | 3.63  | 450  | 0.9473          | 0.7123   |
| 0.1678        | 3.83  | 475  | 0.9788          | 0.7032   |


### Framework versions

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