transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:facebook/deit-tiny-patch16-224 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_conflu_deneme_fold5
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: test
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6341463414634146
---

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# hushem_conflu_deneme_fold5

This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9630
- Accuracy: 0.6341

## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 6    | 1.4708          | 0.2439   |
| 1.7951        | 2.0   | 12   | 1.3099          | 0.2439   |
| 1.7951        | 3.0   | 18   | 1.1130          | 0.4146   |
| 1.2772        | 4.0   | 24   | 1.0471          | 0.7073   |
| 1.1124        | 5.0   | 30   | 1.2680          | 0.5366   |
| 1.1124        | 6.0   | 36   | 1.0908          | 0.5122   |
| 0.9481        | 7.0   | 42   | 1.5674          | 0.3902   |
| 0.9481        | 8.0   | 48   | 0.8947          | 0.6098   |
| 0.9653        | 9.0   | 54   | 1.1885          | 0.6098   |
| 0.639         | 10.0  | 60   | 0.9898          | 0.6585   |
| 0.639         | 11.0  | 66   | 1.7943          | 0.4634   |
| 0.5108        | 12.0  | 72   | 1.7088          | 0.5366   |
| 0.5108        | 13.0  | 78   | 1.6432          | 0.5610   |
| 0.1679        | 14.0  | 84   | 1.5598          | 0.5854   |
| 0.1286        | 15.0  | 90   | 2.1600          | 0.5854   |
| 0.1286        | 16.0  | 96   | 1.9849          | 0.5854   |
| 0.0501        | 17.0  | 102  | 1.9630          | 0.6341   |
| 0.0501        | 18.0  | 108  | 1.9630          | 0.6341   |
| 0.0271        | 19.0  | 114  | 1.9630          | 0.6341   |
| 0.0437        | 20.0  | 120  | 1.9630          | 0.6341   |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
