---
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: 0.8242677824267782
---

<!-- 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: 0.4274
- Accuracy: 0.8243

## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6782        | 1.78  | 15   | 0.5922          | 0.7008   |
| 0.5096        | 3.56  | 30   | 0.5153          | 0.7552   |
| 0.4434        | 5.33  | 45   | 0.4520          | 0.7762   |
| 0.3844        | 7.11  | 60   | 0.4381          | 0.8013   |
| 0.3642        | 8.89  | 75   | 0.4359          | 0.8054   |
| 0.322         | 10.67 | 90   | 0.4086          | 0.8138   |
| 0.2845        | 12.44 | 105  | 0.4111          | 0.8201   |
| 0.2588        | 14.22 | 120  | 0.4100          | 0.8159   |
| 0.2516        | 16.0  | 135  | 0.4122          | 0.8389   |
| 0.2375        | 17.78 | 150  | 0.4085          | 0.8243   |
| 0.2309        | 19.56 | 165  | 0.4149          | 0.8117   |
| 0.2175        | 21.33 | 180  | 0.4274          | 0.8243   |


### Framework versions

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