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---
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
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