YOLO project

Model darknet yolov3 416

directories:

  1. Code:

    project/vehicle_and_person_detection/code
    
    
    for image :
                file: yolo_image.py
    
                command: python yolo_image.py 
    
                for save the detected image: python yolo_image.py --save True
               
                Dependent folder: code/input:- handlabel(40 images) , coco(validation2017) specific to vehicle and person
    
                Takes images from handlabel_images or coco_validation_images 
                save detected bounding box text files handlabel_detected-results or coco_detected-results
                save detected images detected-images
    
    for video : 
                file: yolo_video.py
    
                command: python yolo_output.py --input <file-name> --output 
    
                for save the text (detected bounding) file separately python yolo_output.py --input <file-name> --output --save_file_separately
    
                for save the text (detected bounding) file python yolo_output.py --input <file-name> --output --save_file
    
                video save in out_video folder
                input video path : video (todo) presently code
    
    for mAP :
               file: calculate_map.py
              
               command: python calculate_map.py
    
               folder requirement: coco_ground-truth, coco_detected-results , coco_validation_images(optional)
               result: results/<filename>
    

train :

 file : train.py
 
 command: pthon train.py

 Note: change weights, anchor and classes path in train.py as per the need

credit : https://github.com/qqwweee/keras-yolo3