Package: objectdetection
Tensorflow Object Detection APICreating accurate machine learning models capable of localizing and identifying
multiple objects in a single image remains a core challenge in computer vision.
The TensorFlow Object Detection API is an open source framework built on top of
TensorFlow that makes it easy to construct, train and deploy object detection
models.  At Google we’ve certainly found this codebase to be useful for our
computer vision needs, and we hope that you will as well.Contributions to the codebase are welcome and we would love to hear back from
you if you find this API useful.  Finally if you use the Tensorflow Object
Detection API for a research publication, please consider citing:"Speed/accuracy trade-offs for modern convolutional object detectors."
Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I, Wojna Z,
Song Y, Guadarrama S, Murphy K, CVPR 2017[link][bibtex]MaintainersJonathan Huang, github:jch1Vivek Rathod, github:tombstoneRonny Votel, github:ronnyvotelDerek Chow, github:derekjchowChen Sun, github:jesu9Menglong Zhu, github:dreamdragonAlireza Fathi, github:afathi3Zhichao Lu, github:pkulzcTable of contentsSetup:InstallationQuick Start:Quick Start: Jupyter notebook for off-the-shelf inferenceQuick Start: Training a pet detectorCustomizing a Pipeline:Configuring an object detection pipelinePreparing inputsRunning:Running locallyRunning on the cloudExtras:Tensorflow detection model zooExporting a trained model for inferenceDefining your own model architectureBringing in your own datasetSupported object detection evaluation protocolsInference and evaluation on the Open Images datasetRun an instance segmentation modelRun the evaluation for the Open Images Challenge 2018TPU compatible detection pipelinesRunning object detection on mobile devices with TensorFlow LiteGetting HelpTo get help with issues you may encounter using the Tensorflow Object Detection
API, create a new question onStackOverflowwith
the tags "tensorflow" and "object-detection".Please report bugs (actually broken code, not usage questions) to the
tensorflow/models GitHubissue tracker, prefixing the
issue name with "object_detection".Please checkFAQfor frequently asked questions before
reporting an issue.Release informationSep 17, 2018We have released Faster R-CNN detectors with ResNet-50 / ResNet-101 feature
extractors trained on theiNaturalist Species Detection Dataset.
The models are trained on the training split of the iNaturalist data for 4M
iterations, they achieve 55% and 58% mean AP@.5 over 2854 classes respectively.
For more details please refer to thispaper.Thanks to contributors: Chen SunJuly 13, 2018There are many new updates in this release, extending the functionality and
capability of the API:Moving from slim-based training toEstimator-based
training.Support forRetinaNet, and aMobileNetadaptation of RetinaNet.A novel SSD-based architecture called thePooling Pyramid Network(PPN).Releasing severalTPU-compatible models.
These can be found in thesamples/configs/directory with a comment in the
pipeline configuration files indicating TPU compatibility.Support for quantized training.Updated documentation for new binaries, Cloud training, andTensorflow Lite.See also ourexpanded announcement blogpostand accompanying tutorial at theTensorFlow blog.Thanks to contributors: Sara Robinson, Aakanksha Chowdhery, Derek Chow,
Pengchong Jin, Jonathan Huang, Vivek Rathod, Zhichao Lu, Ronny VotelJune 25, 2018Additional evaluation tools for theOpen Images Challenge 2018are out.
Check out our short tutorial on data preparation and running evaluationhere!Thanks to contributors: Alina KuznetsovaJune 5, 2018We have released the implementation of evaluation metrics for both tracks of theOpen Images Challenge 2018as a part of the Object Detection API - see theevaluation protocolsfor more details.
Additionally, we have released a tool for hierarchical labels expansion for the Open Images Challenge: check outoid_hierarchical_labels_expansion.py.Thanks to contributors: Alina Kuznetsova, Vittorio Ferrari, Jasper UijlingsApril 30, 2018We have released a Faster R-CNN detector with ResNet-101 feature extractor trained onAVAv2.1.
Compared with other commonly used object detectors, it changes the action classification loss function to per-class Sigmoid loss to handle boxes with multiple labels.
The model is trained on the training split of AVA v2.1 for 1.5M iterations, it achieves mean AP of 11.25% over 60 classes on the validation split of AVA v2.1.
For more details please refer to thispaper.Thanks to contributors: Chen Sun, David RossApril 2, 2018Supercharge your mobile phones with the next generation mobile object detector!
We are adding support for MobileNet V2 with SSDLite presented inMobileNetV2: Inverted Residuals and Linear Bottlenecks.
This model is 35% faster than Mobilenet V1 SSD on a Google Pixel phone CPU (200ms vs. 270ms) at the same accuracy.
Along with the model definition, we are also releasing a model checkpoint trained on the COCO dataset.Thanks to contributors: Menglong Zhu, Mark Sandler, Zhichao Lu, Vivek Rathod, Jonathan HuangFebruary 9, 2018We now support instance segmentation!!  In this API update we support a number of instance segmentation models similar to those discussed in theMask R-CNN paper. For further details refer toour slidesfrom the 2017 Coco + Places Workshop.
Refer to the section onRunning an Instance Segmentation Modelfor instructions on how to configure a model
that predicts masks in addition to object bounding boxes.Thanks to contributors: Alireza Fathi, Zhichao Lu, Vivek Rathod, Ronny Votel, Jonathan HuangNovember 17, 2017As a part of the Open Images V3 release we have released:An implementation of the Open Images evaluation metric and theprotocol.Additional tools to separate inference of detection and evaluation (seethis tutorial).A new detection model trained on the Open Images V2 data release (seeOpen Images model).See more information on theOpen Images website!Thanks to contributors: Stefan Popov, Alina KuznetsovaNovember 6, 2017We have re-released faster versions of our (pre-trained) models in themodel zoo.  In addition to what
was available before, we are also adding Faster R-CNN models trained on COCO
with Inception V2 and Resnet-50 feature extractors, as well as a Faster R-CNN
with Resnet-101 model trained on the KITTI dataset.Thanks to contributors: Jonathan Huang, Vivek Rathod, Derek Chow,
Tal Remez, Chen Sun.October 31, 2017We have released a new state-of-the-art model for object detection using
the Faster-RCNN with theNASNet-A image featurization. This
model achieves mAP of 43.1% on the test-dev validation dataset for COCO,
improving on the best available model in the zoo by 6% in terms
of absolute mAP.Thanks to contributors: Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc LeAugust 11, 2017We have released an update to theAndroid Detect
demowhich will now run models trained using the Tensorflow Object
Detection API on an Android device.  By default, it currently runs a
frozen SSD w/Mobilenet detector trained on COCO, but we encourage
you to try out other detection models!Thanks to contributors: Jonathan Huang, Andrew HarpJune 15, 2017In addition to our base Tensorflow detection model definitions, this
release includes:A selection of trainable detection models, including:Single Shot Multibox Detector (SSD) with MobileNet,SSD with Inception V2,Region-Based Fully Convolutional Networks (R-FCN) with Resnet 101,Faster RCNN with Resnet 101,Faster RCNN with Inception Resnet v2Frozen weights (trained on the COCO dataset) for each of the above models to
be used for out-of-the-box inference purposes.AJupyter notebookfor performing
out-of-the-box inference with one of our released modelsConvenientlocal trainingscripts as well as
distributed training and evaluation pipelines viaGoogle Cloud.Thanks to contributors: Jonathan Huang, Vivek Rathod, 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