---
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: smtn_girls_likeOrNot
      split: train
      args: smtn_girls_likeOrNot
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8286558345642541
---

<!-- 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: 0.3887
- Accuracy: 0.8287

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5824        | 0.99  | 42   | 0.5195          | 0.7829   |
| 0.4574        | 2.0   | 85   | 0.4473          | 0.8154   |
| 0.4165        | 2.99  | 127  | 0.3977          | 0.8316   |
| 0.346         | 4.0   | 170  | 0.3881          | 0.8390   |
| 0.3025        | 4.99  | 212  | 0.3950          | 0.8213   |
| 0.3085        | 6.0   | 255  | 0.3965          | 0.8139   |
| 0.2646        | 6.99  | 297  | 0.3895          | 0.8552   |
| 0.3022        | 8.0   | 340  | 0.3828          | 0.8390   |
| 0.2384        | 8.99  | 382  | 0.3878          | 0.8375   |
| 0.2162        | 9.88  | 420  | 0.3887          | 0.8287   |


### Framework versions

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