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Instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth

This codebase implements the loss function described in:

Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth Davy Neven, Bert De Brabandere, Marc Proesmans, and Luc Van Gool Conference on Computer Vision and Pattern Recognition (CVPR), june 2019

Our network architecture is a multi-branched version of ERFNet and uses the Lovasz-hinge loss for maximizing the IoU of each instance.

<p align="center"> <img src="static/teaser.jpg" /> </p>

License

This software is released under a creative commons license which allows for personal and research use only. For a commercial license please contact the authors. You can view a license summary here.

Getting started

This codebase showcases the proposed loss function on car instance segmentation using the Cityscapes dataset.

Prerequisites

Dependencies:

Training

Training consists out of 2 steps. We first train on 512x512 crops around each object, to avoid computation on background patches. Afterwards, we finetune on larger patches (1024x1024) to account for bigger objects and background features which are not present in the smaller crops.

To generate these crops do the following:

$ CITYSCAPES_DIR=/path/to/cityscapes/ python utils/generate_crops.py

Afterwards start training:

$ CITYSCAPES_DIR=/path/to/cityscapes/ python train.py

Different options can be modified in train_config.py, e.g. to visualize set display=True.

Testing

You can download a pretrained model here. Save this file in the src/pretrained_models/ or adapt the test_config.py file.

To test the model on the Cityscapes validation set run:

$ CITYSCAPES_DIR=/path/to/cityscapes/ python test.py

The pretrained model gets 56.4 AP on the car validation set.

Acknowledgement

This work was supported by Toyota, and was carried out at the TRACE Lab at KU Leuven (Toyota Research on Automated Cars in Europe - Leuven)