Detectron README

Detectron is deprecated. Please see detectron2, a ground-up rewrite of Detectron in PyTorch.

Detectron

Detectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework.

At FAIR, Detectron has enabled numerous research projects, including: Feature Pyramid Networks for Object Detection, Mask R-CNN, Detecting and Recognizing Human-Object Interactions, Focal Loss for Dense Object Detection, Non-local Neural Networks, Learning to Segment Every Thing, Data Distillation: Towards Omni-Supervised Learning, DensePose: Dense Human Pose Estimation In The Wild, and Group Normalization.

Example Mask R-CNN output.

Introduction

The goal of Detectron is to provide a high-quality, high-performance codebase for object detection research. It is designed to be flexible in order to support rapid implementation and evaluation of novel research. Detectron includes implementations of the following object detection algorithms:

Mask R-CNN -- Marr Prize at ICCV 2017
RetinaNet -- Best Student Paper Award at ICCV 2017
Faster R-CNN
RPN
Fast R-CNN
R-FCN

using the following backbone network architectures:

ResNeXt{50,101,152}
ResNet{50,101,152}
Feature Pyramid Networks (with ResNet/ResNeXt)
VGG16

Additional backbone architectures may be easily implemented. For more details about these models, please see References below.

Update

4/2018: Support Group Normalization - see GN/README.md

License

Detectron is released under the Apache 2.0 license. See the NOTICE file for additional details.

Citing Detectron

If you use Detectron in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.

@misc{Detectron2018,
  author =       {Ross Girshick and Ilija Radosavovic and Georgia Gkioxari and
                  Piotr Doll\'{a}r and Kaiming He},
  title =        {Detectron},
  howpublished = {\url{https://github.com/facebookresearch/detectron}},
  year =         {2018}
}

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the Detectron Model Zoo.

Installation

Please find installation instructions for Caffe2 and Detectron in INSTALL.md.

Quick Start: Using Detectron

After installation, please see GETTING_STARTED.md for brief tutorials covering inference and training with Detectron.

Getting Help

To start, please check the troubleshooting section of our installation instructions as well as our FAQ. If you couldn't find help there, try searching our GitHub issues. We intend the issues page to be a forum in which the community collectively troubleshoots problems.

If bugs are found, we appreciate pull requests (including adding Q&A's to FAQ.md and improving our installation instructions and troubleshooting documents). Please see CONTRIBUTING.md for more information about contributing to Detectron.

References

Data Distillation: Towards Omni-Supervised Learning. Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, and Kaiming He. Tech report, arXiv, Dec. 2017.
Learning to Segment Every Thing. Ronghang Hu, Piotr Dollár, Kaiming He, Trevor Darrell, and Ross Girshick. Tech report, arXiv, Nov. 2017.
Non-Local Neural Networks. Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He. Tech report, arXiv, Nov. 2017.
Mask R-CNN. Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick. IEEE International Conference on Computer Vision (ICCV), 2017.
Focal Loss for Dense Object Detection. Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár. IEEE International Conference on Computer Vision (ICCV), 2017.
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour. Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. Tech report, arXiv, June 2017.
Detecting and Recognizing Human-Object Interactions. Georgia Gkioxari, Ross Girshick, Piotr Dollár, and Kaiming He. Tech report, arXiv, Apr. 2017.
Feature Pyramid Networks for Object Detection. Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
Aggregated Residual Transformations for Deep Neural Networks. Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
R-FCN: Object Detection via Region-based Fully Convolutional Networks. Jifeng Dai, Yi Li, Kaiming He, and Jian Sun. Conference on Neural Information Processing Systems (NIPS), 2016.
Deep Residual Learning for Image Recognition. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Conference on Neural Information Processing Systems (NIPS), 2015.
Fast R-CNN. Ross Girshick. IEEE International Conference on Computer Vision (ICCV), 2015.

26.4K ·GitHub

Detectron

archived

What is this?

Detectron is Facebook AI Research's software system implementing state-of-the-art object detection algorithms like Mask R-CNN and RetinaNet, built as a research platform for computer vision.

Is it a library or a full framework?

It's a research codebase/framework written in Python and powered by the Caffe2 deep learning framework, providing model implementations, training and inference scripts.

How do I install and get it running?

Follow the Caffe2 and Detectron installation instructions in INSTALL.md, then use GETTINGSTARTED.md for tutorials on inference and training.

Do I need Caffe2 set up before using this?

Yes, Detectron runs on top of the Caffe2 deep learning framework, so Caffe2 must be installed first.

Is this still maintained and usable today?

No, Detectron is deprecated; the README directs users to Detectron2, a ground-up rewrite in PyTorch, instead.

Which object detection algorithms are included?

Detectron includes Mask R-CNN, RetinaNet, Faster R-CNN, RPN, Fast R-CNN, and R-FCN, usable with backbone networks like ResNeXt, ResNet, Feature Pyramid Networks, and VGG16.

Are there pretrained models available?

Yes, Detectron provides a large Model Zoo with baseline results and trained models available for download.

What research has this codebase enabled?

It powered FAIR projects such as Feature Pyramid Networks, Mask R-CNN, DensePose, Focal Loss for RetinaNet, Non-local Neural Networks, and Group Normalization.

Should I use Detectron or Detectron2 for a new project?

Use Detectron2 instead — Detectron is deprecated and the README explicitly points users to the PyTorch rewrite for current work.

What are similar projects?

VisionHOPE, sam3, EasyOCR, dinov3, detectron2, YOLOX, deep-learning-for-image-processing, opencv_contrib

Which topics is it in?

segmentation, model-serving, image-classification, object-detection, neural-network, image-processing, nvidia, deprecated, computer-vision, cuda, pytorch, benchmark

Creator: facebookresearch
Size: 4.4 MB
Stars: 26.4K
Updated: CommitOct 19
Age: 9y
Commits · 90 Days

Detectron README

Detectron

Introduction

Update

License

Citing Detectron

Model Zoo and Baselines

Installation

Quick Start: Using Detectron

Getting Help

References

Detectron (facebookresearch/Detectron)