---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: raildefectfft2
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: defect
      type: imagefolder
      config: Dhika--defectfft
      split: validation
      args: Dhika--defectfft
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7542857142857143
---

<!-- 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. -->

# raildefectfft2

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 defect dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7207
- Accuracy: 0.7543

## 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: 0.0002
- train_batch_size: 30
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3922        | 0.67  | 10   | 1.1690          | 0.6114   |
| 0.8518        | 1.33  | 20   | 0.8874          | 0.6829   |
| 0.5386        | 2.0   | 30   | 0.7207          | 0.7543   |
| 0.3125        | 2.67  | 40   | 0.8383          | 0.7286   |
| 0.2264        | 3.33  | 50   | 0.8440          | 0.7429   |
| 0.1613        | 4.0   | 60   | 0.8516          | 0.7457   |
| 0.119         | 4.67  | 70   | 1.3625          | 0.6      |
| 0.0972        | 5.33  | 80   | 0.9110          | 0.7429   |
| 0.0844        | 6.0   | 90   | 0.8272          | 0.78     |
| 0.0725        | 6.67  | 100  | 0.8958          | 0.74     |
| 0.0708        | 7.33  | 110  | 1.0972          | 0.7371   |
| 0.041         | 8.0   | 120  | 1.0089          | 0.7629   |
| 0.0312        | 8.67  | 130  | 1.0348          | 0.7629   |
| 0.0401        | 9.33  | 140  | 1.2427          | 0.7257   |
| 0.0271        | 10.0  | 150  | 1.0154          | 0.7543   |
| 0.0328        | 10.67 | 160  | 1.0373          | 0.7714   |
| 0.023         | 11.33 | 170  | 1.0051          | 0.7686   |
| 0.0199        | 12.0  | 180  | 0.9775          | 0.7657   |
| 0.0189        | 12.67 | 190  | 1.0088          | 0.7657   |
| 0.0188        | 13.33 | 200  | 1.1904          | 0.7343   |
| 0.0167        | 14.0  | 210  | 1.2999          | 0.7286   |
| 0.0159        | 14.67 | 220  | 1.1326          | 0.7514   |
| 0.0145        | 15.33 | 230  | 1.1386          | 0.7543   |
| 0.015         | 16.0  | 240  | 1.1441          | 0.7543   |
| 0.0133        | 16.67 | 250  | 1.1544          | 0.7514   |
| 0.0132        | 17.33 | 260  | 1.1629          | 0.7514   |
| 0.0121        | 18.0  | 270  | 1.1708          | 0.7514   |
| 0.0121        | 18.67 | 280  | 1.1773          | 0.7514   |
| 0.0114        | 19.33 | 290  | 1.1831          | 0.7514   |
| 0.0111        | 20.0  | 300  | 1.1883          | 0.7514   |
| 0.011         | 20.67 | 310  | 1.1937          | 0.7514   |
| 0.0103        | 21.33 | 320  | 1.1993          | 0.7514   |
| 0.0103        | 22.0  | 330  | 1.2046          | 0.7514   |
| 0.0103        | 22.67 | 340  | 1.2089          | 0.7514   |
| 0.0096        | 23.33 | 350  | 1.2133          | 0.7514   |
| 0.0095        | 24.0  | 360  | 1.2171          | 0.7514   |
| 0.0096        | 24.67 | 370  | 1.2204          | 0.7514   |
| 0.0093        | 25.33 | 380  | 1.2235          | 0.7486   |
| 0.0091        | 26.0  | 390  | 1.2262          | 0.7486   |
| 0.0092        | 26.67 | 400  | 1.2280          | 0.7514   |
| 0.0089        | 27.33 | 410  | 1.2296          | 0.7514   |
| 0.0092        | 28.0  | 420  | 1.2310          | 0.7514   |
| 0.0089        | 28.67 | 430  | 1.2319          | 0.7486   |
| 0.0089        | 29.33 | 440  | 1.2325          | 0.7486   |
| 0.0088        | 30.0  | 450  | 1.2327          | 0.7486   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3