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Mobile Mask R-CNN

This is a Mask R-CNN implementation with MobileNet V1/V2 as Backbone architecture to be finally able to deploy it on mobile devices such as the Nvidia Jetson TX2. The major changes to the original matterport project are:

  • Add Mobilenet V1 and V2 as backbone options (besides ResNet 50 and 101) + dependencies in the model
  • Make the whole project py2 / py3 compatible (original only works on py3)
  • Investigate Training Setup for Mobilenet V1 and implement it in coco_train.py
  • Add a Speedhack to mold /unmold image functions
  • Make the project lean and focused on COCO + direct training on passed class names (IDs before)
  • Inclue more speed up options to the Model (Light-Head RCNN)
  • Release a trained Mobile_Mask_RCNN Model

Getting Started

  • install required packages (mostly over pip)
  • clone this repository
  • download and setup the COCO Dataset: setup_coco.py
  • inside coco.py subclass Config (defined in config.py) and change model params to your needs
  • train mobile mask r-cnn on COCO with: train_coco.py
  • evaluate your trained model with: eval_coco.py
  • do both interactively with the notebook train_coco.ipynb
  • if you face killed kernels due to memory errors, use bash train.sh for infinite training
  • visualize / control training with tensorboard: cd into your current log dir and run:
    tensorboard --logdir="$(pwd)"
  • inspect your model with notebooks/:
    inspect_data.ipynb,inspect_model.ipynb, inspect_weights.ipynb,detection_demo.ipynb
  • convert keras h5 to tensorflow .pb model file, in notebooks/ run:
    export_model.ipynb

Performance

Mobile Mask R-CNN trained on 512x512 input size

  • 100 Proposals: 0.22 mAP (VOC) @ 250ms
  • 1000 Proposals: 0.25 mAP (VOC) @ 330ms

Requirements

  • numpy
  • scipy
  • Pillow
  • cython
  • matplotlib
  • scikit-image
  • tensorflow>=1.3.0
  • keras>=2.1.5
  • opencv-python
  • h5py
  • imgaug
  • IPython[all]
  • pycocotools