---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: cards-vit-base-patch16-224-finetuned-v1
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.31704202872849796
---

<!-- 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. -->

# cards-vit-base-patch16-224-finetuned-v1

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9972
- Accuracy: 0.3170

## 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: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.7068        | 0.9993 | 378  | 1.9533          | 0.2753   |
| 1.6691        | 1.9987 | 756  | 1.9642          | 0.2864   |
| 1.6278        | 2.9980 | 1134 | 1.9935          | 0.3018   |
| 1.5837        | 4.0    | 1513 | 2.0155          | 0.3077   |
| 1.5263        | 4.9993 | 1891 | 2.0283          | 0.3063   |
| 1.4969        | 5.9987 | 2269 | 2.0026          | 0.3081   |
| 1.5088        | 6.9980 | 2647 | 2.0275          | 0.3098   |
| 1.4623        | 8.0    | 3026 | 2.0096          | 0.3137   |
| 1.4305        | 8.9993 | 3404 | 2.0239          | 0.3154   |
| 1.3895        | 9.9934 | 3780 | 1.9972          | 0.3170   |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.0.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1