---
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: 0.6027397260273972
pipeline_tag: image-classification
---

<!-- 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-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: 1.2384
- Accuracy: 0.6027

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.273         | 0.2   | 25   | 1.2384          | 0.6027   |
| 0.5153        | 0.4   | 50   | 1.4060          | 0.5845   |
| 0.2792        | 0.6   | 75   | 1.3026          | 0.5936   |
| 0.5516        | 0.81  | 100  | 1.3999          | 0.6027   |
| 0.4247        | 1.01  | 125  | 1.2621          | 0.5982   |
| 0.1556        | 1.21  | 150  | 1.5661          | 0.5571   |
| 0.1458        | 1.41  | 175  | 1.3459          | 0.6347   |
| 0.1595        | 1.61  | 200  | 1.5278          | 0.5982   |
| 0.1195        | 1.81  | 225  | 1.5303          | 0.6256   |
| 0.1507        | 2.02  | 250  | 1.7701          | 0.5845   |
| 0.023         | 2.22  | 275  | 1.5354          | 0.6301   |
| 0.028         | 2.42  | 300  | 1.6535          | 0.6301   |
| 0.0698        | 2.62  | 325  | 1.6772          | 0.6438   |
| 0.0516        | 2.82  | 350  | 1.4380          | 0.6804   |
| 0.0136        | 3.02  | 375  | 1.6561          | 0.6484   |
| 0.0325        | 3.23  | 400  | 1.6028          | 0.6621   |
| 0.0149        | 3.43  | 425  | 1.6261          | 0.6621   |
| 0.0082        | 3.63  | 450  | 1.6615          | 0.6621   |
| 0.0093        | 3.83  | 475  | 1.6878          | 0.6530   |

---
### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2