DeepLab2: A TensorFlow Library for Deep Labeling

DeepLab2 is a TensorFlow library for deep labeling, aiming to provide a unified and state-of-the-art TensorFlow codebase for dense pixel labeling tasks, including, but not limited to semantic segmentation, instance segmentation, panoptic segmentation, depth estimation, or even video panoptic segmentation.

Deep labeling refers to solving computer vision problems by assigning a predicted value for each pixel in an image with a deep neural network. As long as the problem of interest could be formulated in this way, DeepLab2 should serve the purpose. Additionally, this codebase includes our recent and state-of-the-art research models on deep labeling. We hope you will find it useful for your projects.

Change logs

09/07/2021: Add numpy implementation of Segmentation and Tracking Quality. Find it here
09/06/2021: Update Panoptic-DeepLab w/ MobileNetv3 backbone results on Cityscapes.
08/13/2021: Open-source MaX-DeepLab-L COCO checkpoints (51.3% PQ on COCO val set).
07/26/2021: Add ViP-DeepLab support for SemKITTI-DVPS.
07/07/2021: KITTI-STEP and MOTChallenge-STEP are ready to use.
06/07/2021: Add hungarian matching support on TPU for MaX-DeepLab, thanks to the help from Jiquan Ngiam and Amil Merchant.
06/01/2021: "Hello, World!", DeepLab2 made publicly available.

Installation

See Installation.

Dataset preparation

The dataset needs to be converted to TFRecord. We provide some examples below.

Some guidances about how to convert your own dataset.

Your Own Dataset

Projects

We list a few projects that use DeepLab2.

Colab Demo

Colab notebook for off-the-shelf inference.

Running DeepLab2

See Getting Started. In short, run the following command:

To run DeepLab2 on GPUs, the following command should be used:

python trainer/train.py \
    --config_file=${CONFIG_FILE} \
    --mode={train | eval | train_and_eval | continuous_eval} \
    --model_dir=${BASE_MODEL_DIRECTORY} \
    --num_gpus=${NUM_GPUS}

Contacts (Maintainers)

Please check FAQ if you have some questions before reporting the issues.

Mark Weber, github: markweberdev
Huiyu Wang, github: csrhddlam
Siyuan Qiao, github: joe-siyuan-qiao
Jun Xie, github: clairexie
Maxwell D. Collins, github: mcollinswisc
YuKun Zhu, github: yknzhu
Liangzhe Yuan, github: yuanliangzhe
Dahun Kim, github: mcahny
Qihang Yu, github: yucornetto
Liang-Chieh Chen, github: aquariusjay

Disclaimer

Note that this library contains our re-implemented DeepLab models in TensorFlow2, and thus may have some minor differences from the published papers (e.g., learning rate).
This is not an official Google product.

Citing DeepLab2

If you find DeepLab2 useful for your project, please consider citing DeepLab2 along with the relevant DeepLab series.

DeepLab2:

@article{deeplab2_2021,
  author={Mark Weber and Huiyu Wang and Siyuan Qiao and Jun Xie and Maxwell D. Collins and Yukun Zhu and Liangzhe Yuan and Dahun Kim and Qihang Yu and Daniel Cremers and Laura Leal-Taixe and Alan L. Yuille and Florian Schroff and Hartwig Adam and Liang-Chieh Chen},
  title={{DeepLab2: A TensorFlow Library for Deep Labeling}},
  journal={arXiv: 2106.09748},
  year={2021}
}

References

Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. "The cityscapes dataset for semantic urban scene understanding." In CVPR, 2016.
Andreas Geiger, Philip Lenz, and Raquel Urtasun. "Are we ready for autonomous driving? the kitti vision benchmark suite." In CVPR, 2012.
Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, and Jurgen Gall. "Semantickitti: A dataset for semantic scene understanding of lidar sequences." In ICCV, 2019.
Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, and Piotr Dollar. "Panoptic segmentation." In CVPR, 2019.
Dahun Kim, Sanghyun Woo, Joon-Young Lee, and In So Kweon. "Video panoptic segmentation." In CVPR, 2020.
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollar, and C Lawrence Zitnick. "Microsoft COCO: Common objects in context." In ECCV, 2014.
Patrick Dendorfer, Aljosa Osep, Anton Milan, Konrad Schindler, Daniel Cremers, Ian Reid, Stefan Roth, and Laura Leal-Taixe. "MOTChallenge: A Benchmark for Single-camera Multiple Target Tracking." IJCV, 2020.

nqxjzm/deeplab2