---
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: 0.7471264367816092
---

<!-- 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: 0.8477
- Accuracy: 0.7471

## 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: 3e-05
- 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: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1159        | 1.1   | 100  | 1.1144          | 0.6118   |
| 0.8183        | 2.2   | 200  | 0.9109          | 0.6882   |
| 0.6829        | 3.3   | 300  | 0.7677          | 0.7235   |
| 0.5575        | 4.4   | 400  | 0.7670          | 0.6765   |
| 0.4644        | 5.49  | 500  | 0.8460          | 0.6647   |
| 0.3096        | 6.59  | 600  | 0.7082          | 0.7529   |
| 0.305         | 7.69  | 700  | 0.6939          | 0.7647   |
| 0.3349        | 8.79  | 800  | 0.7285          | 0.7235   |
| 0.36          | 9.89  | 900  | 0.7664          | 0.7294   |
| 0.3184        | 10.99 | 1000 | 0.6807          | 0.7588   |
| 0.2815        | 12.09 | 1100 | 0.7408          | 0.7353   |
| 0.1745        | 13.19 | 1200 | 0.7528          | 0.7294   |
| 0.1894        | 14.29 | 1300 | 0.7634          | 0.7471   |
| 0.1641        | 15.38 | 1400 | 0.7209          | 0.7647   |
| 0.1932        | 16.48 | 1500 | 0.9091          | 0.7      |
| 0.1609        | 17.58 | 1600 | 0.7208          | 0.7588   |
| 0.132         | 18.68 | 1700 | 0.8487          | 0.7588   |
| 0.1903        | 19.78 | 1800 | 0.7912          | 0.7471   |
| 0.121         | 20.88 | 1900 | 0.6735          | 0.7471   |
| 0.1903        | 21.98 | 2000 | 0.6692          | 0.7824   |
| 0.176         | 23.08 | 2100 | 0.8351          | 0.7176   |
| 0.1186        | 24.18 | 2200 | 0.7318          | 0.7471   |
| 0.1424        | 25.27 | 2300 | 0.7860          | 0.7588   |
| 0.144         | 26.37 | 2400 | 0.7021          | 0.7882   |
| 0.1088        | 27.47 | 2500 | 0.8109          | 0.7471   |
| 0.1019        | 28.57 | 2600 | 0.8157          | 0.7471   |
| 0.0947        | 29.67 | 2700 | 0.8028          | 0.7588   |
| 0.1715        | 30.77 | 2800 | 0.8345          | 0.7471   |
| 0.1046        | 31.87 | 2900 | 0.8578          | 0.7412   |
| 0.1367        | 32.97 | 3000 | 0.7670          | 0.7882   |
| 0.1339        | 34.07 | 3100 | 0.7763          | 0.7647   |
| 0.1194        | 35.16 | 3200 | 0.7727          | 0.7706   |
| 0.151         | 36.26 | 3300 | 0.8272          | 0.7471   |
| 0.0646        | 37.36 | 3400 | 0.7721          | 0.7765   |
| 0.0801        | 38.46 | 3500 | 0.8171          | 0.7529   |
| 0.1038        | 39.56 | 3600 | 0.9464          | 0.7059   |
| 0.16          | 40.66 | 3700 | 0.8005          | 0.7706   |
| 0.1151        | 41.76 | 3800 | 0.8784          | 0.7471   |
| 0.1159        | 42.86 | 3900 | 0.8598          | 0.7471   |
| 0.0575        | 43.96 | 4000 | 0.8543          | 0.7529   |
| 0.164         | 45.05 | 4100 | 0.8659          | 0.7588   |
| 0.1319        | 46.15 | 4200 | 0.8854          | 0.7412   |
| 0.0489        | 47.25 | 4300 | 0.7508          | 0.7588   |
| 0.0678        | 48.35 | 4400 | 0.8784          | 0.7353   |
| 0.0832        | 49.45 | 4500 | 0.7248          | 0.7765   |


### Framework versions

- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1