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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: 0.6839080459770115
---
<!-- 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
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