---
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: 0.8044444444444444
---

<!-- 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: 0.5059
- Accuracy: 0.8044

## 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: 16
- eval_batch_size: 16
- 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5884        | 0.59  | 150  | 0.5623          | 0.7022   |
| 0.4854        | 1.19  | 300  | 0.5428          | 0.7422   |
| 0.5224        | 1.78  | 450  | 0.5069          | 0.7444   |
| 0.4026        | 2.37  | 600  | 0.5105          | 0.7556   |
| 0.4381        | 2.96  | 750  | 0.4564          | 0.7844   |
| 0.3707        | 3.56  | 900  | 0.4668          | 0.7844   |
| 0.3649        | 4.15  | 1050 | 0.4684          | 0.7911   |
| 0.3686        | 4.74  | 1200 | 0.4625          | 0.7867   |
| 0.2984        | 5.34  | 1350 | 0.4404          | 0.8289   |
| 0.3545        | 5.93  | 1500 | 0.4282          | 0.8      |
| 0.2921        | 6.52  | 1650 | 0.5068          | 0.7956   |
| 0.2052        | 7.11  | 1800 | 0.5059          | 0.8044   |


### Framework versions

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