mobilenetv2-yolov3

Tensorflow implementation mobilenetv2-yolov3 inspired by keras-yolo3

Usage

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
  --export           Export binary pb model for tensorflow,which you can put it in tensorflow serving directly

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Train

• Download pascal tfrecords from here.
• Change train.py

  opt = <your session config>
  backbone = <your yolov3 backbone>
  log_dir = <path/to/your/tensorboard/log>
  batch_size = <you batch size>
  train_dataset_path = <path/to/your/train/folder>
  val_dataset_path = <path/to/your/val/folder>
  train_dataset_glob = <train glob>
  val_dataset_glob = <val glob>

Performance

3 times faster than darknet53-yolov3 with alpha=1.4 and higher accuracy

Pascal Dataset

I have packaged a pascal tfrecords for you.See here