The code is documented and designed to be easy to extend. If you use it in your research, please consider referencing this repository. If you work on 3D vision, you might find our recently released [Matterport3D](https://matterport.com/blog/2017/09/20/announcing-matterport3d-research-dataset/) dataset useful as well.
The code is documented and designed to be easy to extend. If you use it in your research, please consider citing this repository (bibtex below). If you work on 3D vision, you might find our recently released [Matterport3D](https://matterport.com/blog/2017/09/20/announcing-matterport3d-research-dataset/) dataset useful as well.
This dataset was created from 3D-reconstructed spaces captured by our customers who agreed to make them publicly available for academic use. You can see more examples [here](https://matterport.com/gallery/).
# Getting Started
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@@ -141,6 +141,19 @@ gradients (sum vs mean across batches and GPUs). Or, maybe the official model us
clipping to avoid this issue. We do use gradient clipping, but don't set it too aggressively.
We found that smaller learning rates converge faster anyway so we go with that.
## Citation
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},
