Prepare Training Data

inputs: raw image files, annotation files
outputs: train.txt

1. Generate your Annotation files and Image files:
   Using graphical image annotation tool: LabelImg v1.7.0 (https://github.com/tzutalin/labelImg/releases/tag/v1.7.0)
   Annotations are saved as XML files in PASCAL VOC format.

path to raw images: ./car_dataset_web/JPEGImages
path to annotation files: ./car_dataset_web/Annotations
All damage types are labeled as ‘crack’.

2. Generate your training dataset.
   Using ./car_dataset_web/preprocessing.ipynb to generate train.txt, which include the paths of raw images and the annotation information. We will use train.txt to train our model.
   Here is the format of train.txt:

One row for one image;
Row format: image_file_path box1 box2 ... boxN;
Box format: x_min,y_min,x_max,y_max,class_id (no space).

Here is an example:

  path/to/img1.jpg 50,100,150,200,0 30,50,200,120,3
  path/to/img2.jpg 120,300,250,600,2

Start Training

inputs: train.txt
outputs: Keras model weights

1. Open folder ./keras-yolo3.

2. Modify train.py: annotation_path, classes_path , anchor path , log_dir. The model weights file is saved at ./logs

3. Start training: python train.py

4. The model weights file are saved at line 86, you can modify the save path if you want:

  model.save_weights(log_dir + 'trained_weights_tiny_final.h5')

Test (Optional)

Test all the images located at './car_dataset_web/JPEGImages, the detection results are saved at '/car_dataset_web/PredictedTinyImages/'. You can modify the path easily in yolo_video.py(line 8, line 22).
Usage: python yolo_video.py --images

Covert Keras model into CoreML model

inputs: Keras model weights file: ./result/trained_weights_tiny_final.h5
outputs: CoreML model file: your_model_name.mlmodel

1. Open ./keras2ml.ipynb , run it step by step.
2. You can change the save path at :

  coreml_model.save('your_model_name.mlmodel')

Load model into our App

1. Open ./YOLOv3-CoreML/YOLOv3-CoreML/YOLOv3-CoreML.xcodeproj
2. Add the .mlmodel model file into YOLOv3-CoreML.xcodeproj
3. Open YOLO.swift, modify the model class at line 23:

  let model = your_model_name()

4. Adjust the confidence threshold and IoU Threshold (Optional)
   Open YOLO.swift, line 11, 12

  let confidenceThreshold: Float = 0.2
  let iouThreshold: Float = 0.2

5. Connect your iPhone with Macbook, follow this reference blog to test the App: https://blog.csdn.net/zhenggaoxing/article/details/79042382