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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: 0.7471264367816092
---
<!-- 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
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