---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-RX2-12
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7391304347826086
---

<!-- 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. -->

# vit-base-patch16-224-RX2-12

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: 0.7887
- Accuracy: 0.7391

## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 12

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3604        | 0.94  | 11   | 1.2834          | 0.4783   |
| 1.2312        | 1.96  | 23   | 1.1356          | 0.6522   |
| 1.0933        | 2.98  | 35   | 1.0386          | 0.6739   |
| 0.936         | 4.0   | 47   | 0.9049          | 0.6739   |
| 0.8011        | 4.94  | 58   | 0.9847          | 0.6087   |
| 0.616         | 5.96  | 70   | 0.9236          | 0.6304   |
| 0.5251        | 6.98  | 82   | 0.8640          | 0.6522   |
| 0.4618        | 8.0   | 94   | 0.8612          | 0.7174   |
| 0.3974        | 8.94  | 105  | 0.8461          | 0.6522   |
| 0.3532        | 9.96  | 117  | 0.7887          | 0.7391   |
| 0.335         | 10.98 | 129  | 0.7995          | 0.7174   |
| 0.3211        | 11.23 | 132  | 0.8058          | 0.7174   |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0