---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21K
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-cards-blackjack
  results: []
---

<!-- 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. -->

# finetuned-cards-blackjack

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 card_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5081
- Accuracy: 0.8696

## 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: 7
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3563        | 0.24  | 100  | 1.1495          | 0.6750   |
| 1.3393        | 0.48  | 200  | 1.0388          | 0.7204   |
| 1.2033        | 0.73  | 300  | 0.9324          | 0.7547   |
| 0.9672        | 0.97  | 400  | 0.8558          | 0.7659   |
| 0.8674        | 1.21  | 500  | 0.8456          | 0.7616   |
| 0.8277        | 1.45  | 600  | 0.7563          | 0.7959   |
| 0.8703        | 1.69  | 700  | 0.8465          | 0.7539   |
| 0.893         | 1.94  | 800  | 0.6881          | 0.8002   |
| 0.9454        | 2.18  | 900  | 0.7211          | 0.8027   |
| 0.8109        | 2.42  | 1000 | 0.6369          | 0.8285   |
| 0.8762        | 2.66  | 1100 | 0.6336          | 0.8396   |
| 0.8034        | 2.91  | 1200 | 0.6580          | 0.8165   |
| 0.5833        | 3.15  | 1300 | 0.5828          | 0.8439   |
| 0.8811        | 3.39  | 1400 | 0.6564          | 0.8259   |
| 0.5639        | 3.63  | 1500 | 0.5737          | 0.8439   |
| 0.639         | 3.87  | 1600 | 0.5609          | 0.8379   |
| 0.6455        | 4.12  | 1700 | 0.5820          | 0.8370   |
| 0.5402        | 4.36  | 1800 | 0.5797          | 0.8345   |
| 0.5311        | 4.6   | 1900 | 0.5511          | 0.8456   |
| 0.5734        | 4.84  | 2000 | 0.5444          | 0.8508   |
| 0.5206        | 5.08  | 2100 | 0.5326          | 0.8636   |
| 0.6272        | 5.33  | 2200 | 0.5478          | 0.8525   |
| 0.5124        | 5.57  | 2300 | 0.5296          | 0.8688   |
| 0.5659        | 5.81  | 2400 | 0.5181          | 0.8705   |
| 0.4212        | 6.05  | 2500 | 0.5200          | 0.8611   |
| 0.4338        | 6.3   | 2600 | 0.5135          | 0.8731   |
| 0.3407        | 6.54  | 2700 | 0.5147          | 0.8722   |
| 0.4043        | 6.78  | 2800 | 0.5081          | 0.8696   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2