---
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: 0.6545454545454545
---

<!-- 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-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: 0.7622
- Accuracy: 0.6545

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9737        | 0.2   | 25   | 1.0076          | 0.5045   |
| 0.862         | 0.4   | 50   | 1.0220          | 0.5045   |
| 0.9064        | 0.6   | 75   | 0.9076          | 0.5591   |
| 0.8528        | 0.81  | 100  | 0.8157          | 0.65     |
| 0.8848        | 1.01  | 125  | 0.8089          | 0.6273   |
| 0.6608        | 1.21  | 150  | 0.8615          | 0.6409   |
| 0.6748        | 1.41  | 175  | 0.8426          | 0.6318   |
| 0.6559        | 1.61  | 200  | 0.8427          | 0.6091   |
| 0.5654        | 1.81  | 225  | 0.8267          | 0.6682   |
| 0.5254        | 2.02  | 250  | 0.7622          | 0.6545   |
| 0.2778        | 2.22  | 275  | 0.9481          | 0.6636   |
| 0.309         | 2.42  | 300  | 0.8998          | 0.6409   |
| 0.2396        | 2.62  | 325  | 0.9171          | 0.6409   |
| 0.2773        | 2.82  | 350  | 1.0582          | 0.6091   |
| 0.2516        | 3.02  | 375  | 0.9362          | 0.6455   |
| 0.1578        | 3.23  | 400  | 0.9264          | 0.6773   |
| 0.0979        | 3.43  | 425  | 0.9470          | 0.6773   |
| 0.0836        | 3.63  | 450  | 0.9941          | 0.6682   |
| 0.126         | 3.83  | 475  | 0.9761          | 0.6864   |


### Framework versions

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