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NVlabs / Wetectron

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weakly-supervised object detection platform.

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Wetectron is a software system that implements state-of-the-art weakly-supervised object detection algorithms.


Project CVPR'20, ECCV'20 | Paper CVPR'20, ECCV'20


Check for installation instructions.

Partial labels

The simulated partial labels (points and scribbles) of COCO can be found at Google-drive or Dropbox.

Please check tools/vis_partial_labels.ipynb for a visualization example.

Model zoo

Check for detailed instructions.

Getting started

Check GETTING_STARTED for detailed instrunctions.

New dataset

If you want to run on your own dataset or use other pre-computed proposals (e.g., Edge Boxes), please check USE_YOUR_OWN_DATA for some tips.


Please also check the documentation of maskrcnn-benchmark for things like abstractions and troubleshooting. If your issues are not present there, feel free to open a new issue.


  1. Sequential back-prop and ResNet models.


Please consider citing following papers in your publications if they help your research.

  title = {Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection},
  author = {Zhongzheng Ren and Zhiding Yu and Xiaodong Yang and Ming-Yu Liu and Yong Jae Lee and Alexander G. Schwing and Jan Kautz},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2020}

  title = {UFO$^2$: A Unified Framework towards Omni-supervised Object Detection},
  author = {Zhongzheng Ren and Zhiding Yu and Xiaodong Yang and Ming-Yu Liu and Alexander G. Schwing and Jan Kautz},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2020}


This code is released under the Nvidia Source Code License.

This project is built upon maskrcnn-benchmark, which is released under MIT License.

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