---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: car_manufacturer_model
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.3394495412844037
---

<!-- 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. -->

# car_manufacturer_model

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7826
- Accuracy: 0.3394

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 7    | 3.1387          | 0.2018   |
| 2.8998        | 2.0   | 14   | 3.1029          | 0.2018   |
| 2.7326        | 3.0   | 21   | 3.0453          | 0.2294   |
| 2.7326        | 4.0   | 28   | 3.0104          | 0.2385   |
| 2.5797        | 5.0   | 35   | 2.9655          | 0.2477   |
| 2.4873        | 6.0   | 42   | 2.9166          | 0.3211   |
| 2.4873        | 7.0   | 49   | 2.9122          | 0.2569   |
| 2.3408        | 8.0   | 56   | 2.8122          | 0.3119   |
| 2.2696        | 9.0   | 63   | 2.8159          | 0.3578   |
| 2.1527        | 10.0  | 70   | 2.8589          | 0.2752   |
| 2.1527        | 11.0  | 77   | 2.8248          | 0.2936   |
| 2.0649        | 12.0  | 84   | 2.7709          | 0.2936   |
| 2.0855        | 13.0  | 91   | 2.8183          | 0.2477   |
| 2.0855        | 14.0  | 98   | 2.7552          | 0.2569   |
| 1.9347        | 15.0  | 105  | 2.7826          | 0.3394   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0