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