---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- renovation
metrics:
- accuracy
model-index:
- name: vit-base-renovation
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: renovation
      type: renovation
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6712328767123288
---

<!-- 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 renovation dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1227
- Accuracy: 0.6712

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3065        | 0.2   | 25   | 1.2953          | 0.4201   |
| 1.1693        | 0.4   | 50   | 1.2178          | 0.4749   |
| 1.1812        | 0.6   | 75   | 1.1296          | 0.4932   |
| 1.0392        | 0.81  | 100  | 1.0653          | 0.5936   |
| 0.9393        | 1.01  | 125  | 1.0614          | 0.5936   |
| 0.7521        | 1.21  | 150  | 1.1803          | 0.5342   |
| 0.6482        | 1.41  | 175  | 0.9854          | 0.6210   |
| 0.6643        | 1.61  | 200  | 1.0757          | 0.5616   |
| 0.7273        | 1.81  | 225  | 1.0664          | 0.5662   |
| 0.6387        | 2.02  | 250  | 0.9146          | 0.6575   |
| 0.3924        | 2.22  | 275  | 0.9536          | 0.6530   |
| 0.3131        | 2.42  | 300  | 1.0534          | 0.6347   |
| 0.299         | 2.62  | 325  | 1.0690          | 0.6256   |
| 0.296         | 2.82  | 350  | 1.1816          | 0.6027   |
| 0.1765        | 3.02  | 375  | 0.9577          | 0.6667   |
| 0.1152        | 3.23  | 400  | 1.0853          | 0.6712   |
| 0.112         | 3.43  | 425  | 1.0749          | 0.6849   |
| 0.1083        | 3.63  | 450  | 1.1111          | 0.6804   |
| 0.0969        | 3.83  | 475  | 1.1227          | 0.6712   |


### Framework versions

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