Inspired by: https://github.com/multimodallearning/pytorch-mask-rcnn

NOTES:

• This project is working with PyTorch 0.4.1. If you'd like to help update this, please feel free to fork and create a PR.
• There are two C-extensions that require the NVIDIA compiler and CUDA support.

System Tested (Linux and NVIDIA GPU required with CUDA/cuDNN):

• CUDA 9.0
• NVIDIA Tesla K80
• Ubuntu 16.04

These sets of Jupyter notebooks may also be run in a Docker container running on CUDA-capable GPU hardware.

• Download the COCO model (base for transfer learning) from google drive. You could also choose to start with the ImageNet model.

Work through the notebooks:

• Setup.ipynb - install the PyTorch extensions and grab a few other tools
• Demo.ipynb - to test setup and perform inference with a base model

1. Choose images with your object(s) of interest
2. Label with the VGG Image Annotator tool (http://www.robots.ox.ac.uk/~vgg/software/via/)

Adult Schoolmaster Snappers (Lutjanus apodus); Source: Florent Charpin, http://reefguide.org/pixhtml/schoolmaster2.html

• Train.ipynb - train on custom-labeled data, supported by a custom PyTorch DataSet class (fish_pytorch_style.py)

If you wish to build PyTorch latest or from a commit, follow one of the two notebooks:

• InstallPyTorchSourceCUDA.ipynb - build from source with CUDA support

• See the original repo for more information beyond the two notebooks. https://github.com/multimodallearning/pytorch-mask-rcnn
• See this original blog post for some of the concepts and processes behind the PyTorch and, subsequently, this repo (the TensorFlow version): https://engineering.matterport.com/splash-of-color-instance-segmentation-with-mask-r-cnn-and-tensorflow-7c761e238b46
• Related project - https://github.com/svanbodegraven/Machine-Learning-Containers
• Related project - https://github.com/Azadehkhojandi/computer-vision-fish-frame-proposal
• Merging VGG annotations - https://github.com/Azadehkhojandi/VGG-Image-Annotator-Json-Merger

TIP: You can run this project inside a Docker image such as the rheartpython/cvdeep public image that has many Deep Learning frameworks preinstalled. (more info at https://github.com/michhar/custom-jupyterhub-linux-vm)