Merge pull request aiskunks#190 from yiyundotchen/main

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CSYE_7370/Final Project/Google_Recaptcha_Recognition-YOLOv5/README.md

@@ -3,44 +3,44 @@
 ## Introduction
 
 ### Team Member
-Yiyun Chen 
-NUID:001557532
+Yiyun Chen   
+NUID:001557532  
 
 ### About Capcha Recognition
 It can be really hard to specify the label of capcha pictures: “Select all images with a bus/stop signs/a bicycle”, and sometimes we human may be judged as AI as we failed to choose all images correctly. I wonder how good a real AI would perform at this job, so I planned to use deep learning algorithms to recognize the label of capcha pictures.
 
 ### Dataset
 #### Original Dataset
-Google Recaptcha Image Dataset
-https://www.kaggle.com/datasets/mikhailma/test-dataset
-Almost 12000 images used in Google Recaptcha V2 collected by category more than 500 of which with manual markup for training object detection model such as YOLO.
-One can use those two txt files and Data1.yaml in folder about_datasets to train it with yolo.
+Google Recaptcha Image Dataset  
+https://www.kaggle.com/datasets/mikhailma/test-dataset  
+Almost 12000 images used in Google Recaptcha V2 collected by category more than 500 of which with manual markup for training object detection model such as YOLO.  
+One can use those two txt files and Data1.yaml in folder about_datasets to train it with yolo.  
 #### My own datasets
-https://github.com/yiyundotchen/Google-recaptcha-data-for-yolo
+https://github.com/yiyundotchen/Google-recaptcha-data-for-yolo  
 ##### Dataset1
-Using Data1.ipynb in folder about_datasets, I selected useful images from original dataset, and made it suitable for yolo training.
-It's smaller than original dataset.
+Using Data1.ipynb in folder about_datasets, I selected useful images from original dataset, and made it suitable for yolo training.  
+It's smaller than original dataset.  
 ##### Dataset2
-As shown in results part, the original data is not good for yolo training. No matter how I tune it, the mAP can't be better than 0.5
-So I made labels for bicycle and bus images myself, and made this dataset2 for yolo training.
-I used Data2.ipynb in folder about_datasets to adjust this dataset.
-One can use Data2.yaml in folder about_datasets to train it with yolo.
+As shown in results part, the original data is not good for yolo training. No matter how I tune it, the mAP can't be better than 0.5  
+So I made labels for bicycle and bus images myself, and made this dataset2 for yolo training.  
+I used Data2.ipynb in folder about_datasets to adjust this dataset.  
+One can use Data2.yaml in folder about_datasets to train it with yolo.  
 
 ### Evaluations
-To evaluate object detection models like R-CNN and YOLO, the mean average precision (mAP) is used. The mAP compares the ground-truth bounding box to the detected box and returns a score. The higher the score, the more accurate the model is in its detections.
-Yolo training function offers mAP and other evaluations. I put the evaluations of my final result(the best model) in folder best_model.
+To evaluate object detection models like R-CNN and YOLO, the mean average precision (mAP) is used. The mAP compares the ground-truth bounding box to the detected box and returns a score. The higher the score, the more accurate the model is in its detections.  
+Yolo training function offers mAP and other evaluations. I put the evaluations of my final result(the best model) in folder best_model.  
 
 
 ## Methods
-I'm using YOLOv5, which means "You Only Look Once".
-Yolo is an algorithm that uses convolutional neural networks for object detection. It improves the detection time given that it predicts objects in real-time and provides accurate results with minimal background errors.
-I used yolo both using Jupyter and Google Colab.
-When using Jupyter to run yolo, remember to install cuda, so it can be run on GPU instead of CPU, which is much faster.
-It's much easier and faster to use Colab. I created more Google accounts so that I can use Colab for free.(Highly recommended!!!!!!!)
+I'm using YOLOv5, which means "You Only Look Once".  
+Yolo is an algorithm that uses convolutional neural networks for object detection. It improves the detection time given that it predicts objects in real-time and provides accurate results with minimal background errors.  
+I used yolo both using Jupyter and Google Colab.   
+When using Jupyter to run yolo, remember to install cuda, so it can be run on GPU instead of CPU, which is much faster.  
+It's much easier and faster to use Colab. I created more Google accounts so that I can use Colab for free.(Highly recommended!!!!!!!)  
 
 ## Results
-The "img" in forms below means to change the width and height into any number which is divisible by 32.
-Those are results for Data1, details are in Data1_using_Jupyter.ipynb and Data1_using_Colab.ipynb in folder trainings and results.
+The "img" in forms below means to change the width and height into any number which is divisible by 32.  
+Those are results for Data1, details are in Data1_using_Jupyter.ipynb and Data1_using_Colab.ipynb in folder trainings and results.  
 |  img   | batch  |  epochs  |  mAP  |
 |  :----:  | :----:  | :----:  | :----:  |
 | 120 | 10 | 500 | 0.437 |
@@ -52,26 +52,26 @@ Those are results for Data1, details are in Data1_using_Jupyter.ipynb and Data1_
 | 480 | 15 | 300 | 0.441 |
 | 128 | 15 | 300 | 0.398 |
 
-Those are results for Data2.
+Those are results for Data2.  
 |  img   | batch  |  epochs  |  mAP_bicycle  |  mAP_chimney  |  mAP_bus  |  mAP  |
 |  :----:  | :----:  | :----:  | :----:  | :----:  | :----:  | :----:  |
 | 640 | 2 | 3 | 0.26 | 0.00321 | 0.468 | 0.244 |
 | 640 | 16 | 400 | 0.759 | 0.586 | 0.906 | 0.75 |
 
-Those are results for the best model.
-![image](best_model/image/results.png)
-![image](best_model/image/val_batch1_pred.jpg)
+Those are results for the best model.  
+![image](https://github.com/aiskunks/Skunks_Skool/blob/main/CSYE_7370/Final%20Project/Google_Recaptcha_Recognition-YOLOv5/best_model/results.png)  
+![image](https://github.com/aiskunks/Skunks_Skool/blob/main/CSYE_7370/Final%20Project/Google_Recaptcha_Recognition-YOLOv5/best_model/val_batch1_pred.jpg)  
 
 ## Prospect
-The images used for training are 120*120, which is really unclear. It must have affected the final result.
-With those images, my best mAP is 0.763, still lower than 0.8.
-I'm planning to preprocess those images to improve the model.
+The images used for training are 120*120, which is really unclear. It must have affected the final result.  
+With those images, my best mAP is 0.763, still lower than 0.8.  
+I'm planning to preprocess those images to improve the model.  
 
 
 ## Reference
 
-https://github.com/ultralytics/yolov5/issues/6916
-https://medium.com/analytics-vidhya/understanding-yolo-and-implementing-yolov3-for-object-detection-5f1f748cc63a
-https://www.youtube.com/watch?v=GRtgLlwxpc4
-https://github.com/ultralytics/yolov5
+https://github.com/ultralytics/yolov5/issues/6916  
+https://medium.com/analytics-vidhya/understanding-yolo-and-implementing-yolov3-for-object-detection-5f1f748cc63a  
+https://www.youtube.com/watch?v=GRtgLlwxpc4  
+https://github.com/ultralytics/yolov5