PedestrianDetection

## Introduction A Keras implementation of YOLOv3 (Tensorflow backend) on [bdd100k dataset](http://bair.berkeley.edu/blog/2018/05/30/bdd/). --- ## Guide 1. Download YOLOv3 weights from [YOLO website](http://pjreddie.com/darknet/yolo/). 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)] ``` --- ## Choice of Anchor Boxes 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. --- ## Training 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. ---