Saturday, July 29, 2017

Supercharge your Computer Vision models with the TensorFlow Object Detection API

 At Google, we develop flexible state-of-the-art machine learning (ML) systems for computer vision that not only can be used to improve our products and services, but also spur progress in the research community. Creating accurate ML models capable of localizing and identifying multiple objects in a single image remains a core challenge in the field, and we invest a significant amount of time training and experimenting with these systems.

Detected objects in a sample image (from the COCO dataset) made by one of our models. Image credit: Michael Mileyoriginal image.
Last October, our in-house object detection system achieved new state-of-the-art results, and placed first in the COCO detection challenge. Since then, this system has generated results for a number of research publications1,2,3,4,5,6,7 and has been put to work in Google products such as NestCam, the similar items and style ideas feature in Image Search and street number and name detection in Street View.

Today we are happy to make this system available to the broader research community via the TensorFlow Object Detection API. This codebase is an open-source framework built on top of TensorFlow that makes it easy to construct, train and deploy object detection models. Our goals in designing this system was to support state-of-the-art models while allowing for rapid exploration and research. Our first release contains the following:
The SSD models that use MobileNet are lightweight, so that they can be comfortably run in real time on mobile devices. Our winning COCO submission in 2016 used an ensemble of the Faster RCNN models, which are more computationally intensive but significantly more accurate. For more details on the performance of these models, see our CVPR 2017 paper.

Are you ready to get started?
We’ve certainly found this code to be useful for our computer vision needs, and we hope that you will as well. Contributions to the codebase are welcome and please stay tuned for our own further updates to the framework. To get started, download the code here and try detecting objects in some of your own images using the Jupyter notebook, or training your own pet detector on Cloud ML engine!

Acknowledgements
The release of the Tensorflow Object Detection API and the pre-trained model zoo has been the result of widespread collaboration among Google researchers with feedback and testing from product groups. In particular we want to highlight the contributions of the following individuals:

  • Core Contributors: Derek Chow, Chen Sun, Menglong Zhu, Matthew Tang, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Jasper Uijlings, Viacheslav Kovalevskyi, Kevin Murphy
  • Also special thanks to: Andrew Howard, Rahul Sukthankar, Vittorio Ferrari, Tom Duerig, Chuck Rosenberg, Hartwig Adam, Jing Jing Long, Victor Gomes, George Papandreou, Tyler Zhu

References
  1. Speed/accuracy trade-offs for modern convolutional object detectorsHuang et al., CVPR 2017 (paper describing this framework)
  2. Towards Accurate Multi-person Pose Estimation in the WildPapandreou et al., CVPR 2017
  3. YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in VideoReal et al., CVPR 2017 (see also our blog post)
  4. Beyond Skip Connections: Top-Down Modulation for Object DetectionShrivastava et al., arXiv preprint arXiv:1612.06851, 2016
  5. Spatially Adaptive Computation Time for Residual NetworksFigurnov et al., CVPR 2017
  6. AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual ActionsGu et al., arXiv preprint arXiv:1705.08421, 2017
  7. MobileNets: Efficient convolutional neural networks for mobile vision applicationsHoward et al., arXiv preprint arXiv:1704.04861, 2017

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