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.
- install required packages (mostly over pip)
- clone this repository
- download and setup the COCO Dataset:
setup_coco.py
- inside
coco.py
subclassConfig
(defined inconfig.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
- convert keras h5 to tensorflow .pb model file, in
notebooks/
run:
export_model.ipynb
- numpy
- scipy
- Pillow
- cython
- matplotlib
- scikit-image
- tensorflow>=1.3.0
- keras>=2.1.5
- opencv-python
- h5py
- imgaug
- IPython[all]
- pycocotools