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ClassifyDefectMap_MixedWM38

(★ 11)

Classification of wafer defect map patterns

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ZIP 다운로드

- - args.yaml
  - confusion_matrix.png
  - confusion_matrix_normalized.png
  - results.csv
  - results.png
  - train_batch0.jpg
  - train_batch1.jpg
  - train_batch2.jpg
  - val_batch0_labels.jpg
  - val_batch0_pred.jpg
  - val_batch1_labels.jpg
  - val_batch1_pred.jpg
  - val_batch2_labels.jpg
  - val_batch2_pred.jpg
- - args.yaml
  - class_indices.yaml
  - confusion.csv
  - confusion_matrix.png
  - confusion_matrix_normalized.png
  - custom_confusion_matrix.xlsx
  - metrics-OLD.yaml
  - metrics.yaml
  - model_structure-2.png
  - model_structure-3.png
  - model_structure.png
  - result_confusion_matrix.png
  - results.csv
  - results.png
  - tp_fp_fn_counts.yaml
  - train_batch0.jpg
  - train_batch1.jpg
  - train_batch2.jpg
  - val_batch0_labels.jpg
  - val_batch0_pred.jpg
  - val_batch1_labels.jpg
  - val_batch1_pred.jpg
  - val_batch2_labels.jpg
  - val_batch2_pred.jpg
- - args.yaml
  - confusion_matrix.png
  - confusion_matrix_normalized.png
  - results.csv
  - results.png
  - train_batch0.jpg
  - train_batch1.jpg
  - train_batch2.jpg
  - val_batch0_labels.jpg
  - val_batch0_pred.jpg
  - val_batch1_labels.jpg
  - val_batch1_pred.jpg
  - val_batch2_labels.jpg
  - val_batch2_pred.jpg
.gitignore
encodings.txt
encodings.yaml
MixedWM38_Kaggle.pdf
README.md
val_and_results.py
Wafer Map.png
wafer_defect_map.py
yolo_wafer_classification.py

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jsDelivr

jsDelivr는 공개 GitHub 리포지토리를 별도 설정 없이 CDN으로 즉시 서빙합니다. 버전과 파일을 고르면 웹페이지에 바로 붙일 수 있는 링크와 예시 코드가 만들어집니다.

버전

파일

링크

예시

// repository documentation

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This is a demo project built for personal use using the [MixedWM38 dataset](https://github.com/Junliangwangdhu/WaferMap). Note that there is an issue with the dataset as pointed out in [this issue](https://github.com/Junliangwangdhu/WaferMap/issues/2), which was corrected for the results shared here.

## Wafer map patterns
![image](https://raw.githubusercontent.com/Burhan-Q/ClassifyDefectMap_MixedWM38/master/Wafer%20Map.png)

# Model

Uses the Ultralytics YOLOv8-Large classification model, with standard pretrained weights. The training was run for a short 10 epochs as this was only as a demo project.

# Results

## Confusion Matrix Result
![image](https://raw.githubusercontent.com/Burhan-Q/ClassifyDefectMap_MixedWM38/master/wafer_defects/EXP00002/result_confusion_matrix.png)

## Loss and Accuracy Plots
![image](https://raw.githubusercontent.com/Burhan-Q/ClassifyDefectMap_MixedWM38/master/wafer_defects/EXP00002/results.png)

Overall results from `EXP0002` which was a full GPU training with validation experiment, see the `args.yaml` file to view configuration. Additional metrics were computed using [val_and_results.py](/val_and_results.py). This result should not be considered complete, as model should be trained for additional epochs and additional hyperparameters explored. It is merely a demonstration of implementing a classifier model on wafer defect maps.