This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model is modified to be trained to detect facades and extract their color.
The repository includes:
- Source code of Mask R-CNN built on FPN and ResNet101.
We're providing pre-trained weights for MS COCO to make it easier to start. You can
use those weights as a starting point to train your own variation on the network.
Training and evaluation code is in samples/coco/coco.py. You can import this
module in Jupyter notebook (see the provided notebooks for examples) or you
can run it directly from the command line as such:
# Train a new model starting from pre-trained COCO weights
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=coco
# Train a new model starting from ImageNet weights
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=imagenet
# Continue training a model that you had trained earlier
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=/path/to/weights.h5
# Continue training the last model you trained. This will find
# the last trained weights in the model directory.
python3 samples/coco/coco.py train --dataset=/path/to/coco/ --model=last
You can also run the COCO evaluation code with:
# Run COCO evaluation on the last trained model
python3 samples/coco/coco.py evaluate --dataset=/path/to/coco/ --model=last
The training schedule, learning rate, and other parameters should be set in samples/coco/coco.py.
Start by reading this blog post about the balloon color splash sample. It covers the process starting from annotating images to training to using the results in a sample application.
In summary, to train the model on your own dataset you'll need to extend two classes:
Config
This class contains the default configuration. Subclass it and modify the attributes you need to change.
Dataset
This class provides a consistent way to work with any dataset.
It allows you to use new datasets for training without having to change
the code of the model. It also supports loading multiple datasets at the
same time, which is useful if the objects you want to detect are not
all available in one dataset.
This implementation follows the Mask RCNN paper for the most part, but there are a few cases where we deviated in favor of code simplicity and generalization. These are some of the differences we're aware of. If you encounter other differences, please do let us know.
Python 3.7, TensorFlow 1.3, Keras 2.0.8 and other common packages listed in requirements.txt.
To train or test on MS COCO, you'll also need:
- pycocotools (installation instructions below)
- MS COCO Dataset
- Download the 5K minival and the 35K validation-minus-minival subsets. More details in the original Faster R-CNN implementation.
-
Clone this repository
-
Install dependencies
pip3 install -r requirements.txt
-
Run setup from the repository root directory
python3 setup.py install
-
Download pre-trained COCO weights (mask_rcnn_coco.h5) from the releases page.
-
(Optional) To train or test on MS COCO install
pycocotoolsfrom one of these repos. They are forks of the original pycocotools with fixes for Python3 and Windows (the official repo doesn't seem to be active anymore).- Linux: https://github.com/waleedka/coco
- Windows: https://github.com/philferriere/cocoapi. You must have the Visual C++ 2015 build tools on your path (see the repo for additional details)
- Create your conda environment with the name you select
- There is a environment.yml file you can use to replicate the conda environment!
- conda create -n myenv python=3.7
- Install the required dependencies to your environment
- pip3 install -r requirements.txt
- !pip install h5py==2.10.0
- conda install scikit-image=0.16.2
Use this bibtex to cite this repository:
@misc{matterport_maskrcnn_2017,
title={Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow},
author={Waleed Abdulla},
year={2017},
publisher={Github},
journal={GitHub repository},
howpublished={\url{https://github.com/matterport/Mask_RCNN}},
}