Skip to content
master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.

README.md

bayesian-yolov3

YOLOv3 object detection architecture with uncertainty estimation in TensorFlow.

Accompanying code for: https://arxiv.org/abs/1905.10296 (IEEE Xplore: https://ieeexplore.ieee.org/document/8917494/)

Citation

If you find this work useful in your research, please consider citing:

@inproceedings{kraus_uncertainty_2019,
  address = {Auckland, New Zealand},
  title = {Uncertainty {Estimation} in {One}-{Stage} {Object} {Detection}},
  url = {https://ieeexplore.ieee.org/document/8917494/},
  doi = {10.1109/ITSC.2019.8917494},
  booktitle = {2019 {IEEE} {Intelligent} {Transportation} {Systems} {Conference} ({ITSC})},
  publisher = {IEEE},
  author = {Kraus, Florian and Dietmayer, Klaus},
  month = oct,
  year = {2019},
  pages = {53--60}
}

Notes

  • Training examples with documentation:
    • pretraining.py - pretraining for models with uncertainty estimation
    • uncertainty_training.py - training for models with uncertainty estimation, use checkpoints produced by pretraining.py as a starting point.
    • yolov3_training.py - standard yolov3 without any uncertainty estimation
    • look for "edit" comments
  • Forward passes:
    • detect.py (processes a list of images)
    • inference_*.py scripts. They process tfrecord files and produce ECP (euro city persons) formated json files.
    • Note NMS yields up to 1000 boxes, might be slow. Change the "nms" functions if you want better performance.
    • Current NMS implementation ignores classes. Example code used in the paper is given as comments (only works for two classes).
    • look for "edit" comments
  • tfrecords format:
    • same as for the tensorflow object detection API, however we also support tfrecords files where the label ids start at 0 instead of 1. This is controlled by setting "implicit_background_class" to True (start at 1) or False (start at 0).
    • example script to create tfrecordsfile is provided (create_tf_records_citypersons.py)
  • Pretrained yolov3 weights:
    • you need to download the "darknet53.conv.74" from the original yolov3 site (pjreddie).
  • Most things you can change should be marked with an "edit" comment.

About

YOLOv3 object detection architecture with uncertainty estimation.

Resources

License

Releases

No releases published

Packages

No packages published

Languages

You can’t perform that action at this time.