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Supercharge your Computer Vision models with the TensorFlow Object Detection API
Thursday, June 15, 2017
Posted by Jonathan Huang, Research Scientist and Vivek Rathod, Software Engineer
(Cross-posted on the
Google Open Source Blog
)
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 Miley
,
original 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 publications
1,2,3,4,5,6,7
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:
A selection of trainable detection models, including:
Single Shot Multibox Detector
(SSD) with
MobileNets
SSD with
Inception V2
Region-Based Fully Convolutional Networks
(R-FCN) with
Resnet 101
Faster RCNN
with Resnet 101
Faster RCNN
with
Inception Resnet v2
Frozen weights (trained on the
COCO dataset
) for each of the above models to be used for out-of-the-box inference purposes.
A
Jupyter notebook
for performing out-of-the-box inference with one of our released models
Convenient local training scripts as well as distributed training and evaluation pipelines via Google Cloud
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
Speed/accuracy trade-offs for modern convolutional object detectors
,
Huang et al., CVPR 2017 (paper describing this framework)
Towards Accurate Multi-person Pose Estimation in the Wild
,
Papandreou et al., CVPR 2017
YouTube-BoundingBoxes: A Large High-Precision Human-Annotated Data Set for Object Detection in Video
,
Real et al., CVPR 2017 (see also our
blog post
)
Beyond Skip Connections: Top-Down Modulation for Object Detection
,
Shrivastava et al., arXiv preprint arXiv:1612.06851, 2016
Spatially Adaptive Computation Time for Residual Networks
,
Figurnov et al., CVPR 2017
AVA: A Video Dataset of Spatio-temporally Localized Atomic Visual Actions
,
Gu et al., arXiv preprint arXiv:1705.08421, 2017
MobileNets: Efficient convolutional neural networks for mobile vision applications
,
Howard et al., arXiv preprint arXiv:1704.04861, 2017
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