---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: raildefectfft1
  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: 0.7914285714285715
---

<!-- 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. -->

# raildefectfft1

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: 0.7259
- Accuracy: 0.7914

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3927        | 0.67  | 10   | 1.1308          | 0.6429   |
| 0.8111        | 1.33  | 20   | 0.9788          | 0.6629   |
| 0.513         | 2.0   | 30   | 0.7938          | 0.74     |
| 0.2943        | 2.67  | 40   | 0.8517          | 0.7343   |
| 0.2029        | 3.33  | 50   | 0.7300          | 0.7686   |
| 0.1629        | 4.0   | 60   | 0.7259          | 0.7914   |
| 0.1131        | 4.67  | 70   | 0.9103          | 0.7314   |
| 0.0955        | 5.33  | 80   | 0.8504          | 0.7657   |
| 0.0547        | 6.0   | 90   | 1.0702          | 0.72     |
| 0.0489        | 6.67  | 100  | 1.1708          | 0.6971   |
| 0.0382        | 7.33  | 110  | 1.2376          | 0.6943   |
| 0.0356        | 8.0   | 120  | 1.3361          | 0.6857   |
| 0.0311        | 8.67  | 130  | 1.1809          | 0.7229   |
| 0.0346        | 9.33  | 140  | 1.3405          | 0.7086   |
| 0.0378        | 10.0  | 150  | 1.1800          | 0.7171   |
| 0.0326        | 10.67 | 160  | 1.1292          | 0.7343   |
| 0.0319        | 11.33 | 170  | 1.0885          | 0.7371   |
| 0.0347        | 12.0  | 180  | 1.4550          | 0.6771   |
| 0.0283        | 12.67 | 190  | 1.1957          | 0.7314   |
| 0.0336        | 13.33 | 200  | 1.4648          | 0.6743   |
| 0.0175        | 14.0  | 210  | 1.4927          | 0.6771   |
| 0.0167        | 14.67 | 220  | 1.3760          | 0.7057   |
| 0.0149        | 15.33 | 230  | 1.2464          | 0.7229   |
| 0.0154        | 16.0  | 240  | 1.2553          | 0.7257   |
| 0.0135        | 16.67 | 250  | 1.2768          | 0.7314   |
| 0.0133        | 17.33 | 260  | 1.2857          | 0.7343   |
| 0.0122        | 18.0  | 270  | 1.2905          | 0.7314   |
| 0.0121        | 18.67 | 280  | 1.2929          | 0.7314   |
| 0.0115        | 19.33 | 290  | 1.2958          | 0.7314   |
| 0.0111        | 20.0  | 300  | 1.2985          | 0.7314   |
| 0.011         | 20.67 | 310  | 1.3020          | 0.7343   |
| 0.0103        | 21.33 | 320  | 1.3051          | 0.7371   |
| 0.0103        | 22.0  | 330  | 1.3075          | 0.7371   |
| 0.0104        | 22.67 | 340  | 1.3098          | 0.7371   |
| 0.0096        | 23.33 | 350  | 1.3128          | 0.7371   |
| 0.0095        | 24.0  | 360  | 1.3154          | 0.7371   |
| 0.0096        | 24.67 | 370  | 1.3162          | 0.7371   |
| 0.0093        | 25.33 | 380  | 1.3183          | 0.7371   |
| 0.0091        | 26.0  | 390  | 1.3200          | 0.7371   |
| 0.0092        | 26.67 | 400  | 1.3213          | 0.7371   |
| 0.0089        | 27.33 | 410  | 1.3219          | 0.7371   |
| 0.0092        | 28.0  | 420  | 1.3224          | 0.7371   |
| 0.0089        | 28.67 | 430  | 1.3228          | 0.7371   |
| 0.0089        | 29.33 | 440  | 1.3231          | 0.7371   |
| 0.0089        | 30.0  | 450  | 1.3233          | 0.7371   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3