---
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: 0.6454545454545455
---

<!-- 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: 1.1838
- Accuracy: 0.6455

## 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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9741        | 0.2   | 25   | 0.9575          | 0.4818   |
| 0.9827        | 0.4   | 50   | 0.9344          | 0.5182   |
| 0.8578        | 0.6   | 75   | 0.8343          | 0.6182   |
| 0.9373        | 0.81  | 100  | 0.8896          | 0.5909   |
| 0.7462        | 1.01  | 125  | 0.7969          | 0.6364   |
| 0.6953        | 1.21  | 150  | 0.8157          | 0.6364   |
| 0.5461        | 1.41  | 175  | 0.7634          | 0.6773   |
| 0.6445        | 1.61  | 200  | 0.7743          | 0.6545   |
| 0.5437        | 1.81  | 225  | 0.7717          | 0.65     |
| 0.5911        | 2.02  | 250  | 0.8339          | 0.6364   |
| 0.2483        | 2.22  | 275  | 0.8596          | 0.6318   |
| 0.378         | 2.42  | 300  | 0.9897          | 0.6182   |
| 0.2742        | 2.62  | 325  | 0.8965          | 0.6909   |
| 0.1898        | 2.82  | 350  | 1.0262          | 0.6682   |
| 0.2116        | 3.02  | 375  | 1.1058          | 0.6409   |
| 0.0702        | 3.23  | 400  | 1.0473          | 0.6545   |
| 0.0566        | 3.43  | 425  | 1.0962          | 0.6682   |
| 0.0775        | 3.63  | 450  | 1.1502          | 0.65     |
| 0.0485        | 3.83  | 475  | 1.1838          | 0.6455   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3