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
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
Mobile Mask R-CNN trained on 512x512 input size
100 Proposals: 0.22 mAP (VOC) @ 250ms 1000 Proposals: 0.25 mAP (VOC) @ 330ms
numpy scipy Pillow cython matplotlib scikit-image tensorflow>=1.3.0 keras>=2.1.5 opencv-python h5py imgaug IPython[all] pycocotools