---
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: 0.7102137767220903
---

<!-- 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: 0.6785
- Accuracy: 0.7102

## 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: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6169        | 0.63  | 150  | 0.5812          | 0.6841   |
| 0.4724        | 1.27  | 300  | 0.5037          | 0.7553   |
| 0.5545        | 1.9   | 450  | 0.5195          | 0.7553   |
| 0.3928        | 2.53  | 600  | 0.5964          | 0.7102   |
| 0.3937        | 3.16  | 750  | 0.4637          | 0.7933   |
| 0.3897        | 3.8   | 900  | 0.4548          | 0.8076   |
| 0.356         | 4.43  | 1050 | 0.5327          | 0.7648   |
| 0.319         | 5.06  | 1200 | 0.5126          | 0.7720   |
| 0.3526        | 5.7   | 1350 | 0.6785          | 0.7102   |


### Framework versions

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