---
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-RU5-10-8
  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: 0.7833333333333333
---

<!-- 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-RU5-10-8

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: 0.6773
- Accuracy: 0.7833

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3605        | 0.95  | 14   | 1.2370          | 0.5167   |
| 1.2314        | 1.97  | 29   | 1.0511          | 0.6833   |
| 0.968         | 2.98  | 44   | 0.8919          | 0.65     |
| 0.8135        | 4.0   | 59   | 0.7702          | 0.7667   |
| 0.616         | 4.95  | 73   | 0.7533          | 0.75     |
| 0.5167        | 5.97  | 88   | 0.6773          | 0.7833   |
| 0.4063        | 6.98  | 103  | 0.6974          | 0.75     |
| 0.3401        | 8.0   | 118  | 0.7438          | 0.75     |
| 0.3007        | 8.95  | 132  | 0.6646          | 0.7833   |
| 0.3154        | 9.49  | 140  | 0.6819          | 0.7833   |


### Framework versions

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