README.md

---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: swin-tiny-patch4-window7-224-crack-detector
  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:
        accuracy: 0.9384615384615385
    - name: F1
      type: f1
      value:
        f1: 0.9382975252490704
    - name: Precision
      type: precision
      value:
        precision: 0.9382005688460371
    - name: Recall
      type: recall
      value:
        recall: 0.9395073274524703
---

<!-- 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. -->

# swin-tiny-patch4-window7-224-crack-detector

This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1807
- Accuracy: {'accuracy': 0.9384615384615385}
- F1: {'f1': 0.9382975252490704}
- Precision: {'precision': 0.9382005688460371}
- Recall: {'recall': 0.9395073274524703}

## 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: 5e-05
- 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy                         | F1                         | Precision                         | Recall                         |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:--------------------------:|:---------------------------------:|:------------------------------:|
| 0.3863        | 1.0   | 195  | 0.3349          | {'accuracy': 0.885576923076923}  | {'f1': 0.8829318618369404} | {'precision': 0.8830357915066687} | {'recall': 0.8864842943431257} |
| 0.2685        | 2.0   | 390  | 0.2715          | {'accuracy': 0.9080128205128205} | {'f1': 0.9106277459775055} | {'precision': 0.9130231253775549} | {'recall': 0.9148104520472664} |
| 0.2235        | 3.0   | 585  | 0.1807          | {'accuracy': 0.9384615384615385} | {'f1': 0.9382975252490704} | {'precision': 0.9382005688460371} | {'recall': 0.9395073274524703} |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0
- Datasets 2.17.1
- Tokenizers 0.15.2