---
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: 0.8158995815899581
---

<!-- 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: 0.4121
- Accuracy: 0.8159

## 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: 8
- total_train_batch_size: 256
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6839        | 0.89  | 15   | 0.6438          | 0.6757   |
| 0.5555        | 1.78  | 30   | 0.5198          | 0.7364   |
| 0.4995        | 2.67  | 45   | 0.5212          | 0.7469   |
| 0.4177        | 3.56  | 60   | 0.4447          | 0.7866   |
| 0.415         | 4.44  | 75   | 0.4438          | 0.7929   |
| 0.3737        | 5.33  | 90   | 0.4302          | 0.7866   |
| 0.3588        | 6.22  | 105  | 0.4452          | 0.7992   |
| 0.3343        | 7.11  | 120  | 0.4666          | 0.7908   |
| 0.3095        | 8.0   | 135  | 0.4727          | 0.7720   |
| 0.2951        | 8.89  | 150  | 0.4162          | 0.8138   |
| 0.2819        | 9.78  | 165  | 0.4299          | 0.8159   |
| 0.257         | 10.67 | 180  | 0.4497          | 0.8033   |
| 0.2625        | 11.56 | 195  | 0.4642          | 0.7971   |
| 0.2287        | 12.44 | 210  | 0.4121          | 0.8159   |


### Framework versions

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