1 changed files with 41 additions and 123 deletions
--- a/readme.md
+++ b/readme.md
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 # 지능화 캡스톤 프로젝트 #1 - WDI-CNN
 ### *Wafer Map 데이터를 9종류의 Class로 분류하는 CNN 모델 만들기*
 -----
 [PINBlog Gitea Repository](https://gitea.pinblog.codes/CBNU/03_WDI_CNN)
 -----
 ### 논문
-A Deep Convolutional Neural Network for Wafer Defect Identification on an Imbalanced Dataset in Semiconductor Manufacturing Processes 
+반도체 제조공정의 불균형 데이터셋에 대한 웨이퍼 불량 식별을 위한 심층 컨볼루션 신경망
-(반도체 제조공정의 불균형 데이터셋에 대한 웨이퍼 불량 식별을 위한 심층 컨볼루션 신경망)
+* 번역본
 https://gitea.pinblog.codes/attachments/9b2424f7-7e7d-4ad1-a368-86a523d67504
-* [번역본](https://gitea.pinblog.codes/attachments/9b2424f7-7e7d-4ad1-a368-86a523d67504)
+* 원본
 https://gitea.pinblog.codes/attachments/9a31bb80-bc0a-4d5a-83b1-4ef0557456ad
 * [원본](https://gitea.pinblog.codes/attachments/9a31bb80-bc0a-4d5a-83b1-4ef0557456ad)
 * 인용된 논문 리스트
 | 번호 | 논문 제목 | 저자 | 출판사 및 링크 |
 |------|-----------|------|----------------|
 | 1    | Deep Residual Learning for Image Recognition | Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun | IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. [링크](https://arxiv.org/abs/1512.03385) |
 | 2    | Very Deep Convolutional Networks for Large-Scale Image Recognition | Karen Simonyan, Andrew Zisserman | International Conference on Learning Representations (ICLR), 2015. [링크](https://arxiv.org/abs/1409.1556) |
 | 3    | Going Deeper with Convolutions | Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich | IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [링크](https://arxiv.org/abs/1409.4842) |
 | 4    | Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift | Sergey Ioffe, Christian Szegedy | International Conference on Machine Learning (ICML), 2015. [링크](https://arxiv.org/abs/1502.03167) |
 | 5    | Dropout: A Simple Way to Prevent Neural Networks from Overfitting | Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov | Journal of Machine Learning Research (JMLR), 2014. [링크](http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf) |
 -----
 ### Dataset
 [Kaggle - WDI Data](https://www.kaggle.com/qingyi/wm811k-wafer-map/code)
@ -40,33 +25,19 @@ A Deep Convolutional Neural Network for Wafer Defect Identification on an Imbala
 -----
 ### 수행방법
 * 위 논문을 참고하여 CNN 모델을 구현하고, 
  WDI Dataset을 학습하여 9개의 클래스로 분류한다.
-| 클래스 | 라벨 | Train 이미지 개수 | Validation 이미지 개수 | Test 이미지 개수 |
+  (Center, Donut, Edge-Loc, Edge-Ring, Loc, Near-full, none, Random, Scratch)
 |--------|------|-------------------|------------------------|------------------|
 | None   | 0    | 117,431           | 15,000                 | 15,000           |
 | Center | 1    | 3,294             | 500                    | 500              |
 | Donut  | 2    | 444               | 50                     | 50               |
 | Edge-Loc   | 3    | 4,189             | 500                    | 500              |
 | Edge-Ring   |4     |7,680             |1,000                   |1,000             |
 | Local   |5    	|2,794            	|400                   	|400              |
 | Random  	|6    	|666               	|100                   	|100              |
 | Scratch  	|7    	|894               	|150                   	|150              |
 | Near-full  	|8    	|149               	    |-                  	    |-            |
-  [프로젝트 관련 자료](https://gitea.pinblog.codes/CBNU/03_WDI_CNN/releases/tag/info)
+  https://gitea.pinblog.codes/CBNU/03_WDI_CNN/releases/tag/info
 # Model
 <details>
 <summary>Code View</summary>
 <div markdown="1">
-````python
+```python
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@ -128,17 +99,12 @@ class CNN_WDI(nn.Module):
        return F.softmax(x, dim=1)
 cnn_wdi = CNN_WDI(class_num=9)
 ````
-</div>
+```
 </details>
 # Load Data
 <details>
 <summary>Code View</summary>
 <div markdown="1">
-````python 
+```python
 from torchvision import transforms, datasets
 # 데이터 전처리
@ -158,17 +124,11 @@ data_transforms = transforms.Compose([
 train_dataset = datasets.ImageFolder(root='E:/wm_images/train/', transform=data_transforms)
 val_dataset = datasets.ImageFolder(root='E:/wm_images/val/', transform=data_transforms)
 test_dataset = datasets.ImageFolder(root='E:/wm_images/test/', transform=data_transforms)
-````
+```
 </div>
 </details>
 # Settings
 <details>
 <summary>Code View</summary>
 <div markdown="1">
-````python
+```python
 import torch.optim as optim
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
@ -189,17 +149,12 @@ num_epochs = 100 #* 192
 # Random sample size
 train_max_images = 95
 val_max_images = 25
 ````
-</div>
+```
 </details>
 # Train Function
 <details>
 <summary>Code View</summary>
 <div markdown="1">
-````python
+```python
 # 학습 함수 정의
 def train(model, dataloader, criterion, optimizer, device):
    model.train()
@ -226,17 +181,11 @@ def train(model, dataloader, criterion, optimizer, device):
    epoch_acc = running_corrects.double() / len(dataloader.dataset)
    return epoch_loss, epoch_acc
-````
+```
 </div>
 </details>
 # Evaluate Function
 <details>
 <summary>Code View</summary>
 <div markdown="1">
-````python
+```python
 # 평가 함수 정의
 def evaluate(model, dataloader, criterion, device):
    model.eval()
@ -259,17 +208,12 @@ def evaluate(model, dataloader, criterion, device):
        epoch_acc = running_corrects.double() / len(dataloader.dataset)
    return epoch_loss, epoch_acc
-````
+```
 </div>
 </details>
 # Train
 <details>
 <summary>Code View</summary>
 <div markdown="1">
-````python
+
 ```python
 # Train & Validation의 Loss, Acc 기록 파일
 s_title = 'Epoch,\tTrain Loss,\tTrain Acc,\tVal Loss,\tVal Acc\n'
 with open('output.txt', 'a') as file:
@ -299,25 +243,19 @@ for epoch in range(num_epochs + 1):
    if epoch % 10 == 0:
        # 모델 저장
        torch.save(cnn_wdi.state_dict(), 'CNN_WDI_' + str(epoch) + 'epoch.pth')
-````
+```
 </div>
 </details>
 -----
 ### 평가방법
 * 모델의 성능지표(Precision, Recall, Accuracy, F1-Score)를 혼동행렬(Confusion Metrix)로 구현한다.
 # Confusion Metrix
 <details>
 <summary>Code View</summary>
 <div markdown="1">
-````python
+```python
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
@ -397,17 +335,12 @@ def predict_and_plot_metrics(title, model, dataloader, criterion, device):
    plot_metrics(title, class_names, precisions, recalls, f1_scores, epoch_acc.item())
    return epoch_loss, epoch_acc, report
 ````
-</div>
+```
 </details>
 # Evaluate
 <details>
 <summary>Code View</summary>
 <div markdown="1">
-````python 
+```python
 import os
 import re
@ -430,17 +363,11 @@ for model in sorted_models:
    # Call the predict_and_plot_metrics function with the appropriate arguments
    epoch_loss, epoch_acc, report = predict_and_plot_metrics(model, cnn_wdi, test_loader, criterion, device)
    # print(f'Model: {model} Test Loss: {test_loss:.4f} Acc: {test_acc:.4f}')
-````
+```
 </div>
 </details>
 # Loss Graph
 <details>
 <summary>Code View</summary>
 <div markdown="1">
-````python
+```python
 import matplotlib.pyplot as plt
 # 파일에서 데이터를 읽어들입니다.
@ -478,17 +405,12 @@ plt.ylabel('Values')
 plt.title('Training and Validation Loss and Accuracy')
 plt.legend()
 plt.show()
 ````
-</div>
+```
 </details>
 # Print Selecting Test Model Result
 <details>
 <summary>Code View</summary>
 <div markdown="1">
-````python 
+```python
 def output(model, dataloader, criterion, device):
    model.eval()
    running_loss = 0.0
@ -538,14 +460,10 @@ def output(model, dataloader, criterion, device):
 selected_model = 'CNN_WDI_20epoch.pth'
 cnn_wdi.load_state_dict(torch.load(selected_model))
 output(cnn_wdi, test_loader, criterion, device)
-````
+```
 </div>
 </details>
 -----
 ### 테스트 결과
 [1차 테스트](https://gitea.pinblog.codes/CBNU/03_WDI_CNN/wiki/1%EC%B0%A8-%ED%85%8C%EC%8A%A4%ED%8A%B8_%EC%9B%90%EB%B3%B8-%EB%8D%B0%EC%9D%B4%ED%84%B0-%ED%95%99%EC%8A%B5)