---
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-RU2-10
  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: 0.8166666666666667
---

<!-- 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-RU2-10

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: 0.6802
- Accuracy: 0.8167

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1641        | 0.99  | 38   | 0.9789          | 0.7333   |
| 0.5847        | 2.0   | 77   | 0.6371          | 0.8167   |
| 0.2844        | 2.99  | 115  | 0.6706          | 0.75     |
| 0.2275        | 4.0   | 154  | 0.5359          | 0.8167   |
| 0.1539        | 4.99  | 192  | 0.6067          | 0.8167   |
| 0.1113        | 6.0   | 231  | 0.7887          | 0.7667   |
| 0.1117        | 6.99  | 269  | 0.6443          | 0.8167   |
| 0.1088        | 8.0   | 308  | 0.6429          | 0.85     |
| 0.0824        | 8.99  | 346  | 0.6499          | 0.8333   |
| 0.0834        | 9.87  | 380  | 0.6802          | 0.8167   |


### Framework versions

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