/Object_detection_demo

A demo about how to use darknet to build a object detection model.

Introduction

Darknet encapsulates object detection and image classification models, which gives us a user-friendly interface to deploy advanced deep learning models. Here I use Darknet to perform coral reef detection in inner hackathon.
The page gives detailed information about how to install Darknet, training YOLO on VOC or COCO datasets.
The following steps following Training YOLO on VOC part.

Procedures

  • Build Darknet in GPU mode
    • If you don't know the cuda path, try whereis nvcc
    • OpenCV error
  • Prepare the training data YOLO needs
    • Refer to https://pjreddie.com/media/files/voc_label.py. For each image, there is a txt file contains several lines that format like this <object-class> <x> <y> <width> <height>. Each line represents a object in the image.
  • Create three config files
    • Model configuration file: darknet/cfg/your_file_name.cfg
      • Copy darknet/cfg/yolov3.cfg to darknet/cfg/your_file_name.cfg, revise Line 7, 8.. refer from here
    • Data configuration file: darknet/cfg/your_file_name.data
      • Copy darknet/cfg/voc.data to darknet/cfg/your_file_name.data, revise all lines.
      • The backup folder stores trained models.
      • The names stores the labels of training data specified in the following Data label file.
    • Data label file: darknet/data/your_file_name.names
      • Copy darknet/data/voc.names to darknet/data/your_file_name.names, each row corresponding to one label.
  • Train the model
    • Execute ./darknet detector train cfg/your_file_name.data cfg/your_file_name.cfg darknet53.conv.74， where darknet53.conv.74 is pretrained model weights.
  • Evulate the model
    • Compute the recall: ./darknet detector recall data/your_file_name.names cfg/your_file_name.cfg your_model_x00.weights.
    • If ERROR data/coco_val_5k.list is not found happens, here is the solution.
  • Use the model
    • Execute ./darknet detector test cfg/your_file_name.data cfg/your_file_name.cfg your_model_x00.weights your_test_image_path.
    • The result of above command is predictions.png.