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---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
widget:
- src: https://huggingface.co/alkzar90/croupier-creature-classifier/resolve/main/examples/crusader_peco_peco.png
example_title: Crusader-Rangarok-Online
- src: https://huggingface.co/alkzar90/croupier-creature-classifier/resolve/main/examples/goblin_wow.png
example_title: Goblin-WoW
- src: https://huggingface.co/alkzar90/croupier-creature-classifier/resolve/main/examples/dobby_harry_potter.jpg
example_title: Dobby-Harry-Potter
- src: https://huggingface.co/alkzar90/croupier-creature-classifier/resolve/main/examples/resident_evil_nemesis.jpeg
example_title: Nemesis-Resident-Evil
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: 0.7583
- 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: 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6663 | 1.1 | 100 | 1.0179 | 0.5941 |
| 0.4924 | 2.2 | 200 | 0.7036 | 0.7529 |
| 0.4552 | 3.3 | 300 | 0.6123 | 0.7824 |
| 0.2355 | 4.4 | 400 | 0.5748 | 0.7647 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|