The keras-yolo3 from qiangzhu123

keras-yolo3's Introduction

Quick Start

Download YOLOv3 weights from YOLO website.
Convert the Darknet YOLO model to a Keras model.
Run YOLO detection.

wget https://pjreddie.com/media/files/yolov3.weights
python convert.py yolov3.cfg yolov3.weights model_data/yolo.h5
python yolo_video.py [OPTIONS...] --image, for image detection mode, OR
python yolo_video.py [video_path] [output_path (optional)]

For Tiny YOLOv3, just do in a similar way, just specify model path and anchor path with --model model_file and --anchors anchor_file.

Usage

Use --help to see usage of yolo_video.py:

usage: yolo_video.py [-h] [--model MODEL] [--anchors ANCHORS]
                     [--classes CLASSES] [--gpu_num GPU_NUM] [--image]
                     [--input] [--output]

positional arguments:
  --input        Video input path
  --output       Video output path

optional arguments:
  -h, --help         show this help message and exit
  --model MODEL      path to model weight file, default model_data/yolo.h5
  --anchors ANCHORS  path to anchor definitions, default
                     model_data/yolo_anchors.txt
  --classes CLASSES  path to class definitions, default
                     model_data/coco_classes.txt
  --gpu_num GPU_NUM  Number of GPU to use, default 1
  --image            Image detection mode, will ignore all positional arguments

MultiGPU usage: use --gpu_num N to use N GPUs. It is passed to the Keras multi_gpu_model(). ##训练数据要求跟其他模型不同，它需要是一个文本文件，每行包含一个样本，所以对一些数据集要先将其转换为文本格式 path/to/img1.jpg 50,100,150,200,0 30,50,200,120,3 path/to/img2.jpg 120,300,250,600,2 对于已有的权重，考虑是否需要将其转化为h5格式数据， python convert.py -w yolov3.cfg yolov3.weights model_data/yolo_weights.h5

其训练脚本不同于命令行形式，他将需要的参数放入了train.py文件中，所以如果要训练，需要修改train.py文件包括--classes class_file 和--anchors anchor_file，这两个文件要按照数据集的情况修改

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