---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: croupier-creature-classifier
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: croupier-mtg-dataset
      type: imagefolder
      config: alkzar90--croupier-mtg-dataset
      split: train
      args: alkzar90--croupier-mtg-dataset
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8058823529411765
---

<!-- 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. -->

# croupier-creature-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 croupier-mtg-dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6480
- Accuracy: 0.8059

## 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: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1967        | 1.1   | 100  | 0.6480          | 0.8059   |
| 0.1047        | 2.2   | 200  | 0.8703          | 0.7529   |
| 0.2249        | 3.3   | 300  | 0.9539          | 0.7588   |
| 0.0984        | 4.4   | 400  | 0.9319          | 0.7529   |
| 0.086         | 5.49  | 500  | 0.9061          | 0.7706   |
| 0.1164        | 6.59  | 600  | 0.7493          | 0.8176   |
| 0.0518        | 7.69  | 700  | 0.8781          | 0.7765   |
| 0.0458        | 8.79  | 800  | 0.8851          | 0.7824   |
| 0.0521        | 9.89  | 900  | 0.9448          | 0.7882   |
| 0.0576        | 10.99 | 1000 | 0.8884          | 0.7824   |
| 0.0442        | 12.09 | 1100 | 0.8965          | 0.7882   |
| 0.0254        | 13.19 | 1200 | 0.9140          | 0.7882   |
| 0.0426        | 14.29 | 1300 | 0.9274          | 0.7882   |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1