Tutorial-by-Alex

Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark.

What's New

• It is powered by the PyTorch deep learning framework.
• Includes more features such as panoptic segmentation, densepose, Cascade R-CNN, rotated bounding boxes, etc.
• Can be used as a library to support different projects on top of it. We'll open source more research projects in this way.
• It trains much faster.

See our blog post to see more demos and learn about detectron2.

Installation

See INSTALL.md.

Quick Start

See GETTING_STARTED.md, or the Colab Notebook.

Learn more at our documentation. And see projects/ for some projects that are built on top of detectron2.

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the Detectron2 Model Zoo.

Citing Detectron2

If you use Detectron2 in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.

@misc{wu2019detectron2,
  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and
                  Wan-Yen Lo and Ross Girshick},
  title =        {Detectron2},
  howpublished = {\url{https://github.com/facebookresearch/detectron2}},
  year =         {2019}
}

Predict in ROS (Docker)

ROS wrapper for Mask-RCNN.

Installation

Mask-RCNN uses Python 3.
If you use a ROS version built with Python 2, additional steps are necessary to run the node.

You need to build the cv_bridge module of ROS with Python 3.
I recommend using a workspace separate from other ROS packages. Clone the package to the workspace.
You might need to adjust some of the following instructions depending on your Python installation.

# Have already in this repo.
git clone -b melodic https://github.com/ros-perception/vision_opencv.git 

• First method catkin_make (recommend),

  source catkin_make.sh

• Second method for script,

  source how_to_solve_python3_cv2.sh

Check your system can use cv_bridge in python3 (import getCvType).

python3  
  
from cv_bridge.boost.cv_bridge_boost import getCvType  

How to run

# environment
source environment.sh  
  
# download model
source download_model.sh

# download dataset
cd datasets
python3 download_dataset.py   
  
# launch  
roslaunch rcnn_pkg mask_rcnn_prediction.launch