---
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: 0.8389955686853766
---

<!-- 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: 0.3983
- Accuracy: 0.8390

## 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: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5745        | 0.99  | 42   | 0.5208          | 0.7829   |
| 0.4617        | 2.0   | 85   | 0.4346          | 0.8065   |
| 0.4245        | 2.99  | 127  | 0.4151          | 0.8346   |
| 0.3512        | 4.0   | 170  | 0.3854          | 0.8508   |
| 0.3146        | 4.99  | 212  | 0.4062          | 0.8360   |
| 0.3235        | 6.0   | 255  | 0.3864          | 0.8390   |
| 0.2699        | 6.99  | 297  | 0.4094          | 0.8508   |
| 0.3049        | 8.0   | 340  | 0.3735          | 0.8567   |
| 0.2459        | 8.99  | 382  | 0.4037          | 0.8360   |
| 0.2277        | 9.88  | 420  | 0.3983          | 0.8390   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0