---
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: 0.6839080459770115
---

<!-- 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: 1.1036
- Accuracy: 0.6839

## 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: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1638        | 1.1   | 100  | 1.0564          | 0.5471   |
| 0.8524        | 2.2   | 200  | 0.9403          | 0.6118   |
| 0.8231        | 3.3   | 300  | 0.8282          | 0.7176   |
| 0.7398        | 4.4   | 400  | 0.9056          | 0.6294   |
| 0.41          | 5.49  | 500  | 0.8815          | 0.6235   |
| 0.4849        | 6.59  | 600  | 0.9505          | 0.6294   |
| 0.3894        | 7.69  | 700  | 0.8052          | 0.6882   |
| 0.4678        | 8.79  | 800  | 0.8424          | 0.7059   |
| 0.4279        | 9.89  | 900  | 0.9639          | 0.6706   |
| 0.3461        | 10.99 | 1000 | 0.8497          | 0.7059   |
| 0.2741        | 12.09 | 1100 | 0.9090          | 0.7      |
| 0.1771        | 13.19 | 1200 | 0.8292          | 0.7118   |
| 0.1779        | 14.29 | 1300 | 1.1314          | 0.6294   |
| 0.2044        | 15.38 | 1400 | 0.8349          | 0.7294   |
| 0.1543        | 16.48 | 1500 | 0.8952          | 0.6941   |
| 0.1283        | 17.58 | 1600 | 0.8054          | 0.7353   |
| 0.1721        | 18.68 | 1700 | 0.9094          | 0.7235   |
| 0.1509        | 19.78 | 1800 | 0.9168          | 0.7412   |
| 0.1257        | 20.88 | 1900 | 0.9395          | 0.7412   |
| 0.1747        | 21.98 | 2000 | 0.8746          | 0.7471   |
| 0.1506        | 23.08 | 2100 | 0.7992          | 0.7353   |
| 0.1021        | 24.18 | 2200 | 0.7446          | 0.7706   |


### Framework versions

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