Tagging Images - Setup guide for multiple users to do tagging and share them

1. Put this folder in C drive so its like (C:/VoTT/sourcesimages)
2. run vott-2.1.0-win32.exe
3. open vott app after installed
4. Setup security key in settings : SECURITY_KEY
5. open 'Yolo.vott' in 'targetimages' folder using vott app
6. label
7. share the 'sourceimages' with each other so we have unified tagged images

Image Annotation - Setup Tagging Images for Training

1. Tag images
2. export VoTT
3. Put CSV and images into Data/Source_Images/Training_Images/vott-csv-export, and copy of images into Data/Source_Images/Training_Images/
4. Run 'Convert_to_YOLO_format.py' (this generates the "train.txt" in vott-csv-export)
5. Run 'Download_and_Convert_YOLO_weights.py' (unnecessary I think but do to be sure, get from https://pjreddie.com/media/files/yolov3.weights)

Training - Training the model

1. Run 'Train_YOLO.py'
2. Wait a LONG time...

Inference - Analysis of source images

1. Run 'Detector.py'
2. Check 'TrainYourOwnYOLO/Data/Source_Images/Test_Images/Test_Image_Detection_Results' for analysis results

Converter - Post analysis with the intent on feeding back in results to improve the models accuracy

1. Check for valid images, moving "bad" ones to "Data/Source_Images/Test_Image_Detection_Results_Bad/" so they can be removed by the sanitizer script in the next step
2. Run "DetectionResultSanitizer.py" to create "Detection_Results_Sanitized.csv"
3. Optionally: Run "FixSinaSillyAssFilename.py" for cleaning up incorrect file names, edit as required but it will remove "result" and convert spaces to %20 as expected (if you're like Sina and messed up thousands of file names and want me to solve the mess lol)
4. Run "savantConverter.py" to create "exported.csv" to re-train AI using the correctly tagged images from analysis (see step 3 of "Setup Tagging Images for Training")