---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: attraction-classifier
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8133047210300429
---

<!-- 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. -->

# attraction-classifier

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5427
- Accuracy: 0.8133

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 69
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.49          | 1.15  | 150  | 0.5494          | 0.7232   |
| 0.4285        | 2.29  | 300  | 0.4832          | 0.7768   |
| 0.461         | 3.44  | 450  | 0.4907          | 0.7618   |
| 0.3535        | 4.58  | 600  | 0.4597          | 0.7811   |
| 0.2758        | 5.73  | 750  | 0.5102          | 0.7790   |
| 0.2705        | 6.87  | 900  | 0.4669          | 0.8004   |
| 0.2614        | 8.02  | 1050 | 0.4598          | 0.8004   |
| 0.2213        | 9.16  | 1200 | 0.4797          | 0.8112   |
| 0.1682        | 10.31 | 1350 | 0.5601          | 0.7876   |
| 0.144         | 11.45 | 1500 | 0.4544          | 0.8155   |
| 0.1269        | 12.6  | 1650 | 0.4904          | 0.8262   |
| 0.1638        | 13.74 | 1800 | 0.5052          | 0.8197   |
| 0.0869        | 14.89 | 1950 | 0.5427          | 0.8133   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0