---
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: 0.8021680216802168
---

<!-- 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: 0.5632
- Accuracy: 0.8022

## 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: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6613        | 0.48  | 100  | 0.6067          | 0.6477   |
| 0.6115        | 0.97  | 200  | 0.5579          | 0.6992   |
| 0.5542        | 1.45  | 300  | 0.5501          | 0.7182   |
| 0.4758        | 1.93  | 400  | 0.5108          | 0.7534   |
| 0.5219        | 2.42  | 500  | 0.5122          | 0.7561   |
| 0.4631        | 2.9   | 600  | 0.4842          | 0.7832   |
| 0.3866        | 3.38  | 700  | 0.5298          | 0.7480   |
| 0.3704        | 3.86  | 800  | 0.4963          | 0.7453   |
| 0.4222        | 4.35  | 900  | 0.4832          | 0.7561   |
| 0.3162        | 4.83  | 1000 | 0.4807          | 0.7778   |
| 0.2686        | 5.31  | 1100 | 0.4949          | 0.7859   |
| 0.304         | 5.8   | 1200 | 0.4719          | 0.7751   |
| 0.2246        | 6.28  | 1300 | 0.5014          | 0.8157   |
| 0.2503        | 6.76  | 1400 | 0.5077          | 0.8103   |
| 0.169         | 7.25  | 1500 | 0.4630          | 0.8238   |
| 0.2248        | 7.73  | 1600 | 0.5329          | 0.7832   |
| 0.164         | 8.21  | 1700 | 0.5608          | 0.7859   |
| 0.208         | 8.7   | 1800 | 0.5632          | 0.8022   |


### Framework versions

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