---
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: 0.7902542372881356
---

<!-- 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: 0.5746
- Accuracy: 0.7903

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4968        | 1.13  | 150  | 0.5187          | 0.7627   |
| 0.4266        | 2.26  | 300  | 0.4863          | 0.7627   |
| 0.3521        | 3.38  | 450  | 0.5066          | 0.7627   |
| 0.3407        | 4.51  | 600  | 0.4736          | 0.7860   |
| 0.2895        | 5.64  | 750  | 0.5043          | 0.7712   |
| 0.2595        | 6.77  | 900  | 0.6222          | 0.7669   |
| 0.2132        | 7.89  | 1050 | 0.4935          | 0.8008   |
| 0.2156        | 9.02  | 1200 | 0.5229          | 0.7924   |
| 0.192         | 10.15 | 1350 | 0.5168          | 0.7881   |
| 0.1329        | 11.28 | 1500 | 0.5746          | 0.7903   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0