---
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: .faces
      split: train
      args: .faces
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8254437869822485
---

<!-- 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: 0.4452
- Accuracy: 0.8254

## 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
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.622         | 0.99  | 42   | 0.4917          | 0.7825   |
| 0.5018        | 1.99  | 84   | 0.4727          | 0.7840   |
| 0.4308        | 2.98  | 126  | 0.4231          | 0.8254   |
| 0.3811        | 4.0   | 169  | 0.4085          | 0.8254   |
| 0.304         | 4.99  | 211  | 0.4239          | 0.8062   |
| 0.2844        | 5.99  | 253  | 0.4529          | 0.8047   |
| 0.2549        | 6.98  | 295  | 0.4248          | 0.8254   |
| 0.2162        | 8.0   | 338  | 0.4202          | 0.8195   |
| 0.2073        | 8.99  | 380  | 0.4388          | 0.8328   |
| 0.1751        | 9.94  | 420  | 0.4452          | 0.8254   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3