dreamnotover / pedestriandetection Goto Github PK

A Keras implementation of YOLOv3 (Tensorflow backend) on bdd100k dataset.

1. Download YOLOv3 weights from YOLO website.
2. Convert the Darknet YOLO model to Keras model.
3. Run YOLO detection.

wget https://pjreddie.com/media/files/yolov3.weights
python convert.py -w yolov3.cfg yolov3.weights model_data/yolo_weights.h5
python yolo.py   OR   python yolo_video.py [video_path] [output_path(optional)]

YOLO v3, in total uses 9 anchor boxes. Three for each scale. If training YOLO on a custom dataset, generation of anchors must be done using K-Means clustering. Then, arrange the anchors is descending order of dimensions. Assign the three biggest anchors for the first scale , the next three for the second scale, and the last three for the third.

1. Generate the annotation file using python bdd100k_annotation.py and class names file.
   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).
   For example:

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

2. Make sure you have run python convert.py -w yolov3.cfg yolov3.weights model_data/yolo_weights.h5
   The file model_data/yolo_weights.h5 is used to load pretrained weights.

3. Modify train.py and start training.
   python train.py
   Use your trained weights or checkpoint weights in yolo.py.
   Remember to modify class path or anchor path.