---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: camera-type
  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: 0.9915611814345991
---

<!-- 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. -->

# camera-type

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: 0.0235
- Accuracy: 0.9916

## 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: 10
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0064        | 0.4   | 200  | 0.0235          | 0.9916   |
| 0.0034        | 0.79  | 400  | 0.0392          | 0.9941   |
| 0.0066        | 1.19  | 600  | 0.1011          | 0.9840   |
| 0.0           | 1.58  | 800  | 0.1227          | 0.9840   |
| 0.0           | 1.98  | 1000 | 0.1232          | 0.9840   |
| 0.0           | 2.37  | 1200 | 0.1433          | 0.9840   |
| 0.0           | 2.77  | 1400 | 0.1416          | 0.9840   |
| 0.0           | 3.16  | 1600 | 0.1408          | 0.9840   |
| 0.0           | 3.56  | 1800 | 0.1401          | 0.9840   |
| 0.0           | 3.95  | 2000 | 0.1394          | 0.9840   |
| 0.0           | 4.35  | 2200 | 0.1390          | 0.9840   |
| 0.0           | 4.74  | 2400 | 0.1389          | 0.9840   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3