This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model is used to detect masks in a given image and if suitable insert another image into one of the masks. It's based on Feature Pyramid Network (FPN) and a ResNet101 backbone.
We have used pre-trained weights for MS COCO to make it easier to start.
Training and evaluation code is in samples/coco/coco.py.
The training schedule, learning rate, and other parameters should be set in samples/coco/coco.py.
Python 3.4, 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
- More details in the original Faster R-CNN implementation.
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Clone this repository
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Install dependencies
pip3 install -r requirements.txt
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Run setup from the repository root directory
python3 setup.py install
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Download pre-trained COCO weights (mask_rcnn_coco.h5) from the releases page.
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(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)
If you extend this model to other datasets or build projects that use it, we'd love to hear from you.
4K Video Demo by Karol Majek.
Images to OSM: Improve OpenStreetMap by adding baseball, soccer, tennis, football, and basketball fields.
Splash of Color. A blog post explaining how to train this model from scratch and use it to implement a color splash effect.
Segmenting Nuclei in Microscopy Images. Built for the 2018 Data Science Bowl
Code is in the samples/nucleus directory.
Detection and Segmentation for Surgery Robots by the NUS Control & Mechatronics Lab.
A proof of concept project by Esri, in collaboration with Nvidia and Miami-Dade County. Along with a great write up and code by Dmitry Kudinov, Daniel Hedges, and Omar Maher.
A project from Japan to automatically track cells in a microfluidics platform. Paper is pending, but the source code is released.
Research project to understand the complex processes between degradations in the Arctic and climate change. By Weixing Zhang, Chandi Witharana, Anna Liljedahl, and Mikhail Kanevskiy.
A computer vision class project by HU Shiyu to apply the color pop effect on people with beautiful results.
Mapping Challenge: Convert satellite imagery to maps for use by humanitarian organisations.
GRASS GIS Addon to generate vector masks from geospatial imagery. Based on a Master's thesis by Ondřej Pešek.
