---
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-renovation
  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-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 renovations dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7651
- 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: 8

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9092        | 1.67  | 100  | 0.8281          | 0.5686   |
| 0.3809        | 3.33  | 200  | 0.7651          | 0.6667   |
| 0.1873        | 5.0   | 300  | 1.0182          | 0.6667   |
| 0.019         | 6.67  | 400  | 1.2346          | 0.6471   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3