Yes
BOOLEAN
Yes
BOOLEAN
We demonstrate the performance of GluonCV/NLP models in various computer vision and natural language processing tasks. Specifically, we evaluate popular or state-of-the-art models on standard benchmark data sets. In the experiments, we compare model performance between GluonCV/NLP and other open source implementations with Caffe, Caffe2, Theano, and TensorFlow, including ResNet BIBREF8 and MobileNet BIBREF9 for image classification (ImageNet), Faster R-CNN BIBREF10 for object detection (COCO), Mask R-CNN BIBREF11 for instance segmentation, Simple Pose BIBREF12 for pose estimation (COCO), textCNN BIBREF13 for sentiment analysis (TREC), and BERT BIBREF14 for question answering (SQuAD 1.1), sentiment analysis (SST-2), natural langauge inference (MNLI-m), and paraphrasing (MRPC). Table TABREF5 shows that the GluonCV/GluonNLP implementation matches or outperforms the compared open source implementation for the same model evaluated on the same data set.
We demonstrate the performance of GluonCV/NLP models in various computer vision and natural language processing tasks. Specifically, we evaluate popular or state-of-the-art models on standard benchmark data sets. In the experiments, we compare model performance between GluonCV/NLP and other open source implementations with Caffe, Caffe2, Theano, and TensorFlow, including ResNet BIBREF8 and MobileNet BIBREF9 for image classification (ImageNet), Faster R-CNN BIBREF10 for object detection (COCO), Mask R-CNN BIBREF11 for instance segmentation, Simple Pose BIBREF12 for pose estimation (COCO), textCNN BIBREF13 for sentiment analysis (TREC), and BERT BIBREF14 for question answering (SQuAD 1.1), sentiment analysis (SST-2), natural langauge inference (MNLI-m), and paraphrasing (MRPC). Table TABREF5 shows that the GluonCV/GluonNLP implementation matches or outperforms the compared open source implementation for the same model evaluated on the same data set.
1907.04433
Introduction </s> Deep Ġlearning , Ġa Ġsub - field Ġof Ġmachine Ġlearning Ġresearch , Ġhas Ġdriven Ġthe Ġrapid Ġprogress Ġin Ġartificial Ġintelligence Ġresearch , Ġleading Ġto Ġastonishing Ġbreakthrough s Ġon Ġlong - standing Ġproblems Ġin Ġa Ġplethora Ġof Ġfields Ġsuch Ġas Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing . ĠTools Ġpowered Ġby Ġdeep Ġlearning Ġare Ġchanging Ġthe Ġway Ġmovies Ġare Ġmade , Ġdiseases Ġare Ġdiagnosed , Ġand Ġplay Ġa Ġgrowing Ġrole Ġin Ġunderstanding Ġand Ġcommunicating Ġwith Ġhumans . </s> Such Ġdevelopment Ġis Ġmade Ġpossible Ġby Ġdeep Ġlearning Ġframeworks , Ġsuch Ġas ĠC affe ĠB IB REF 0 Ġ, ĠCh ainer ĠB IB REF 1 Ġ, ĠC NT K ĠB IB REF 2 Ġ, ĠApache Ġ( inc ub ating ) ĠMX Net ĠB IB REF 3 Ġ, ĠPy Tor ch ĠB IB REF 4 Ġ, ĠT ensor Flow ĠB IB REF 5 Ġ, Ġand ĠThe ano ĠB IB REF 6 Ġ. ĠThese Ġframeworks Ġhave Ġbeen Ġcrucial Ġin Ġdissemin ating Ġideas Ġin Ġthe Ġfield . ĠSpecifically , Ġimperative Ġtools , Ġarguably Ġspearheaded Ġby ĠCh ainer , Ġare Ġeasy Ġto Ġlearn , Ġread , Ġand Ġdebug . ĠSuch Ġbenefits Ġmake Ġimperative Ġprogramming Ġinterface Ġquickly Ġadopted Ġby Ġthe ĠG lu on ĠAPI Ġof ĠMX Net Ġ( while Ġcan Ġbe Ġseamlessly Ġswitched Ġto Ġsymbolic Ġprogramming Ġfor Ġhigh Ġperformance ), ĠPy Tor ch , Ġand ĠT ensor Flow ĠE ager . </s> L ever aging Ġthe Ġimperative ĠG lu on ĠAPI Ġof ĠMX Net , Ġwe Ġdesign Ġand Ġdevelop Ġthe ĠG lu on CV Ġand ĠG lu on N LP Ġ( re ferred Ġto Ġas ĠG lu on CV / N LP Ġhere ina fter ) Ġtool k its Ġfor Ġdeep Ġlearning Ġin Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing . ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , ĠG lu on CV / N LP Ġare Ġthe Ġfirst Ġopen Ġsource Ġtool k its Ġfor Ġdeep Ġlearning Ġin Ġboth Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing Ġthat Ġsimultaneously Ġi ) Ġprovide Ġmodular ĠAPIs Ġto Ġallow Ġcustomization Ġby Ġre - using Ġefficient Ġbuilding Ġblocks ; Ġii ) Ġprovide Ġpre - trained Ġstate - of - the - art Ġmodels , Ġtraining Ġscripts , Ġand Ġtraining Ġlogs Ġto Ġenable Ġfast Ġprototyp ing Ġand Ġpromote Ġreprodu cible Ġresearch ; Ġiii ) Ġleverage Ġthe ĠMX Net Ġecosystem Ġso Ġthat Ġmodels Ġcan Ġbe Ġdeployed Ġin Ġa Ġwide Ġvariety Ġof Ġprogramming Ġlanguages Ġincluding ĠC ++ , ĠClojure , ĠJava , ĠJulia , ĠPerl , ĠPython , ĠR , Ġand ĠScala . </s> Design Ġand ĠFeatures </s> In Ġthe Ġfollowing , Ġwe Ġdescribe Ġthe Ġdesign Ġand Ġfeatures Ġof ĠG lu on CV / N LP . </s> Mod ular ĠAPIs </s> G lu on CV / N LP Ġprovide Ġaccess Ġto Ġmodular ĠAPIs Ġto Ġallow Ġusers Ġto Ġcustomize Ġtheir Ġmodel Ġdesign , Ġtraining , Ġand Ġinference Ġby Ġre - using Ġefficient Ġcomponents Ġacross Ġdifferent Ġmodels . ĠSuch Ġcommon Ġcomponents Ġinclude Ġ( but Ġare Ġnot Ġlimited Ġto ) Ġdata Ġprocessing Ġutilities , Ġmodels Ġwith Ġindividual Ġcomponents , Ġinitialization Ġmethods , Ġand Ġloss Ġfunctions . </s> To Ġeluc idate Ġhow Ġthe Ġmodular ĠAPI Ġfacilitates Ġefficient Ġimplementation , Ġlet Ġus Ġtake Ġthe Ġdata ĠAPI Ġof ĠG lu on CV / N LP Ġas Ġan Ġexample , Ġwhich Ġis Ġused Ġto Ġbuild Ġefficient Ġdata Ġpipelines Ġwith Ġpopular Ġbenchmark Ġdata Ġsets Ġor Ġthose Ġsupplied Ġby Ġusers . ĠIn Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing Ġtasks , Ġinputs Ġor Ġlabels Ġoften Ġcome Ġin Ġwith Ġdifferent Ġshapes , Ġsuch Ġas Ġimages Ġwith Ġa Ġvarying Ġnumber Ġof Ġobjects Ġand Ġsentences Ġof Ġdifferent Ġlengths . ĠThus , Ġthe Ġdata ĠAPI Ġprovides Ġa Ġcollection Ġof Ġutilities Ġto Ġsample Ġinputs Ġor Ġlabels Ġthen Ġtransform Ġthem Ġinto Ġmini - bat ches Ġto Ġbe Ġefficiently Ġcomputed . ĠBesides , Ġusers Ġcan Ġaccess Ġa Ġwide Ġrange Ġof Ġpopular Ġdata Ġsets Ġvia Ġthe Ġdata ĠAPI , Ġincluding Ġ( but Ġare Ġnot Ġlimited Ġto ) ĠImage Net Ġof Ġimage Ġclassification , ĠV OC Ġof Ġobject Ġdetection , ĠC OC O Ġof Ġinstance Ġsegment ation , ĠS Qu AD Ġof Ġquestion Ġanswering , Ġand ĠS ST Ġof Ġsentiment Ġanalysis . </s> Model ĠZoo </s> Building Ġupon Ġthose Ġmodular ĠAPIs , ĠG lu on CV / N LP Ġprovide Ġpre - trained Ġstate - of - the - art Ġmodels , Ġtraining Ġscripts , Ġand Ġtraining Ġlogs Ġvia Ġthe Ġmodel Ġzoo Ġto Ġenable Ġfast Ġprototyp ing Ġand Ġpromote Ġreprodu cible Ġresearch . ĠAs Ġof Ġthe Ġtime Ġof Ġwriting , ĠG lu on CV / N LP Ġhave Ġprovided Ġover Ġ100 Ġmodels Ġfor Ġcommon Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing Ġtasks , Ġsuch Ġas Ġimage Ġclassification , Ġobject Ġdetection , Ġsemantic Ġsegment ation , Ġinstance Ġsegment ation , Ġpose Ġestimation , Ġword Ġembed ding , Ġlanguage Ġmodel , Ġmachine Ġtranslation , Ġsentiment Ġanalysis , Ġnatural Ġlanguage Ġinference , Ġdependency Ġparsing , Ġand Ġquestion Ġanswering . </s> L ever aging Ġthe ĠMX Net ĠE cosystem </s> G lu on CV / N LP Ġhave Ġbenef itted Ġfrom Ġthe ĠMX Net Ġecosystem Ġthrough Ġuse Ġof ĠMX Net . ĠAt Ġthe Ġlowest Ġlevel , ĠMX Net Ġprovides Ġhigh - performance ĠC ++ Ġimplementations Ġof Ġoperators Ġthat Ġare Ġlever aged Ġby ĠG lu on CV / N LP ; Ġthus , Ġimprovements Ġin Ġlow - level Ġcomponents Ġof ĠMX Net Ġoften Ġresult Ġin Ġperformance Ġgains Ġin ĠG lu on CV / N LP . ĠSame Ġas Ġany Ġother Ġmodel Ġimplemented Ġwith ĠMX Net , ĠG lu on CV / N LP Ġcan Ġbe Ġused Ġto Ġtrain Ġmodels Ġon ĠCPU , ĠGPU Ġ( single Ġor Ġmultiple ), Ġand Ġmultiple Ġmachines . ĠIn Ġsharp Ġcontrast Ġto Ġbuilding Ġupon Ġother Ġdeep Ġlearning Ġframeworks , Ġthrough Ġthe Ġunique Ġhybrid izing Ġmechanism Ġby ĠMX Net ĠB IB REF 7 Ġ, Ġusually ĠG lu on CV / N LP Ġmodels Ġcan Ġbe Ġdeployed Ġwith Ġno Ġor Ġminimal Ġconfiguration Ġin Ġa Ġwide Ġspectrum Ġof Ġprogramming Ġlanguages Ġincluding ĠC ++ , ĠClojure , ĠJava , ĠJulia , ĠPerl , ĠPython , ĠR , Ġand ĠScala . ĠThere Ġare Ġalso Ġongoing Ġefforts Ġto Ġbring Ġmore Ġquant ization Ġ( int 8 Ġand Ġfloat 16 Ġinference ) Ġbenefits Ġfrom ĠMX Net Ġto ĠG lu on CV / N LP Ġto Ġfurther Ġaccelerate Ġmodel Ġinference . </s> The Ġdocumentation Ġhttps :// g lu on - cv . mx net . io / Ġand Ġhttp :// g lu on - nl p . mx net . io / Ġof ĠG lu on CV / N LP Ġinclude Ġinstallation Ġinstructions , Ġcontribution Ġinstructions , Ġopen Ġsource Ġrepositories , Ġextensive ĠAPI Ġreference , Ġand Ġcomprehensive Ġtutorials . ĠAs Ġanother Ġbenefit Ġof Ġleveraging Ġthe ĠMX Net Ġecosystem , Ġthe ĠG lu on CV / N LP Ġdocumentation Ġis Ġsupplemented Ġby Ġthe Ġinteractive Ġopen Ġsource Ġbook ĠDive Ġinto ĠDeep ĠLearning Ġ( based Ġon Ġthe ĠG lu on ĠAPI Ġof ĠMX Net ) ĠB IB REF 7 Ġ, Ġwhich Ġprovides Ġsufficient Ġbackground Ġknowledge Ġabout ĠG lu on CV / N LP Ġtasks , Ġmodels , Ġand Ġbuilding Ġblocks . ĠNotably , Ġsome Ġusers Ġof ĠDive Ġinto ĠDeep ĠLearning Ġhave Ġlater Ġbecome Ġcontributors Ġof ĠG lu on CV / N LP . </s> Requ irement , ĠAvailability , Ġand ĠCommunity </s> G lu on CV / N LP Ġare Ġimplemented Ġin ĠPython Ġand Ġare Ġavailable Ġfor Ġsystems Ġrunning ĠLinux , ĠmacOS , Ġand ĠWindows Ġsince ĠPython Ġis Ġplatform Ġag nostic . ĠThe Ġminimum Ġand Ġopen Ġsource Ġpackage Ġ( e . g ., ĠMX Net ) Ġrequirements Ġare Ġspecified Ġin Ġthe Ġdocumentation . ĠAs Ġof Ġthe Ġtime Ġof Ġwriting , ĠG lu on CV / N LP Ġhave Ġreached Ġversion Ġ0 . 6 Ġand Ġ0 . 4 Ġrespectively , Ġand Ġhave Ġbeen Ġopen Ġsourced Ġunder Ġthe ĠApache Ġ2 . 0 Ġlicense . ĠSince Ġthe Ġinitial Ġrelease Ġof Ġthe Ġsource Ġcode Ġin ĠApril Ġ2018 , ĠG lu on CV / N LP Ġhave Ġattracted Ġ100 Ġcontributors Ġworldwide . ĠModels Ġof ĠG lu on CV / N LP Ġhave Ġbeen Ġdownloaded Ġfor Ġmore Ġthan Ġ1 . 6 Ġmillion Ġtimes Ġin Ġfewer Ġthan Ġ10 Ġmonths . </s> Performance </s> We Ġdemonstrate Ġthe Ġperformance Ġof ĠG lu on CV / N LP Ġmodels Ġin Ġvarious Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing Ġtasks . ĠSpecifically , Ġwe Ġevaluate Ġpopular Ġor Ġstate - of - the - art Ġmodels Ġon Ġstandard Ġbenchmark Ġdata Ġsets . ĠIn Ġthe Ġexperiments , Ġwe Ġcompare Ġmodel Ġperformance Ġbetween ĠG lu on CV / N LP Ġand Ġother Ġopen Ġsource Ġimplementations Ġwith ĠC affe , ĠC affe 2 , ĠThe ano , Ġand ĠT ensor Flow , Ġincluding ĠRes Net ĠB IB REF 8 Ġand ĠMobile Net ĠB IB REF 9 Ġfor Ġimage Ġclassification Ġ( Image Net ), ĠFaster ĠR - CNN ĠB IB REF 10 Ġfor Ġobject Ġdetection Ġ( C OC O ), ĠMask ĠR - CNN ĠB IB REF 11 Ġfor Ġinstance Ġsegment ation , ĠSimple ĠPose ĠB IB REF 12 Ġfor Ġpose Ġestimation Ġ( C OC O ), Ġtext CNN ĠB IB REF 13 Ġfor Ġsentiment Ġanalysis Ġ( T REC ), Ġand ĠB ERT ĠB IB REF 14 Ġfor Ġquestion Ġanswering Ġ( S Qu AD Ġ1 . 1 ), Ġsentiment Ġanalysis Ġ( S ST - 2 ), Ġnatural Ġlang au ge Ġinference Ġ( MN LI - m ), Ġand Ġparaph r asing Ġ( MR PC ). ĠTable ĠT AB REF 5 Ġshows Ġthat Ġthe ĠG lu on CV / G lu on N LP Ġimplementation Ġmatches Ġor Ġoutper forms Ġthe Ġcompared Ġopen Ġsource Ġimplementation Ġfor Ġthe Ġsame Ġmodel Ġevaluated Ġon Ġthe Ġsame Ġdata Ġset . </s> Conclusion </s> G lu on CV / N LP Ġprovide Ġmodular ĠAPIs Ġand Ġthe Ġmodel Ġzoo Ġto Ġallow Ġusers Ġto Ġrapidly Ġtry Ġout Ġnew Ġideas Ġor Ġdevelop Ġdownstream Ġapplications Ġin Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing . ĠG lu on CV / N LP Ġare Ġin Ġactive Ġdevelopment Ġand Ġour Ġfuture Ġworks Ġinclude Ġfurther Ġenrich ing Ġthe ĠAPI Ġand Ġthe Ġmodel Ġzoo , Ġand Ġsupporting Ġdeployment Ġin Ġmore Ġscenarios . </s> We Ġwould Ġlike Ġto Ġthank Ġall Ġthe Ġcontributors Ġof ĠG lu on CV Ġand ĠG lu on N LP Ġ( the Ġgit Ġlog Ġcommand Ġcan Ġbe Ġused Ġto Ġlist Ġall Ġthe Ġcontributors ). ĠSpecifically , Ġwe Ġthank ĠXia oting ĠHe , ĠHe ew on ĠJe on , ĠKang j ian ĠWu , Ġand ĠL uy u ĠXia Ġfor Ġproviding Ġpart Ġof Ġresults Ġin ĠTable ĠT AB REF 5 Ġ. ĠWe Ġwould Ġalso Ġlike Ġto Ġthank Ġthe Ġentire ĠMX Net Ġcommunity Ġfor Ġtheir Ġfoundational Ġcontributions .
Introduction Deep learning, a sub-field of machine learning research, has driven the rapid progress in artificial intelligence research, leading to astonishing breakthroughs on long-standing problems in a plethora of fields such as computer vision and natural language processing. Tools powered by deep learning are changing the way movies are made, diseases are diagnosed, and play a growing role in understanding and communicating with humans. Such development is made possible by deep learning frameworks, such as Caffe BIBREF0 , Chainer BIBREF1 , CNTK BIBREF2 , Apache (incubating) MXNet BIBREF3 , PyTorch BIBREF4 , TensorFlow BIBREF5 , and Theano BIBREF6 . These frameworks have been crucial in disseminating ideas in the field. Specifically, imperative tools, arguably spearheaded by Chainer, are easy to learn, read, and debug. Such benefits make imperative programming interface quickly adopted by the Gluon API of MXNet (while can be seamlessly switched to symbolic programming for high performance), PyTorch, and TensorFlow Eager. Leveraging the imperative Gluon API of MXNet, we design and develop the GluonCV and GluonNLP (referred to as GluonCV/NLP hereinafter) toolkits for deep learning in computer vision and natural language processing. To the best of our knowledge, GluonCV/NLP are the first open source toolkits for deep learning in both computer vision and natural language processing that simultaneously i) provide modular APIs to allow customization by re-using efficient building blocks; ii) provide pre-trained state-of-the-art models, training scripts, and training logs to enable fast prototyping and promote reproducible research; iii) leverage the MXNet ecosystem so that models can be deployed in a wide variety of programming languages including C++, Clojure, Java, Julia, Perl, Python, R, and Scala. Design and Features In the following, we describe the design and features of GluonCV/NLP. Modular APIs GluonCV/NLP provide access to modular APIs to allow users to customize their model design, training, and inference by re-using efficient components across different models. Such common components include (but are not limited to) data processing utilities, models with individual components, initialization methods, and loss functions. To elucidate how the modular API facilitates efficient implementation, let us take the data API of GluonCV/NLP as an example, which is used to build efficient data pipelines with popular benchmark data sets or those supplied by users. In computer vision and natural language processing tasks, inputs or labels often come in with different shapes, such as images with a varying number of objects and sentences of different lengths. Thus, the data API provides a collection of utilities to sample inputs or labels then transform them into mini-batches to be efficiently computed. Besides, users can access a wide range of popular data sets via the data API, including (but are not limited to) ImageNet of image classification, VOC of object detection, COCO of instance segmentation, SQuAD of question answering, and SST of sentiment analysis. Model Zoo Building upon those modular APIs, GluonCV/NLP provide pre-trained state-of-the-art models, training scripts, and training logs via the model zoo to enable fast prototyping and promote reproducible research. As of the time of writing, GluonCV/NLP have provided over 100 models for common computer vision and natural language processing tasks, such as image classification, object detection, semantic segmentation, instance segmentation, pose estimation, word embedding, language model, machine translation, sentiment analysis, natural language inference, dependency parsing, and question answering. Leveraging the MXNet Ecosystem GluonCV/NLP have benefitted from the MXNet ecosystem through use of MXNet. At the lowest level, MXNet provides high-performance C++ implementations of operators that are leveraged by GluonCV/NLP; thus, improvements in low-level components of MXNet often result in performance gains in GluonCV/NLP. Same as any other model implemented with MXNet, GluonCV/NLP can be used to train models on CPU, GPU (single or multiple), and multiple machines. In sharp contrast to building upon other deep learning frameworks, through the unique hybridizing mechanism by MXNet BIBREF7 , usually GluonCV/NLP models can be deployed with no or minimal configuration in a wide spectrum of programming languages including C++, Clojure, Java, Julia, Perl, Python, R, and Scala. There are also ongoing efforts to bring more quantization (int8 and float16 inference) benefits from MXNet to GluonCV/NLP to further accelerate model inference. The documentation https://gluon-cv.mxnet.io/ and http://gluon-nlp.mxnet.io/ of GluonCV/NLP include installation instructions, contribution instructions, open source repositories, extensive API reference, and comprehensive tutorials. As another benefit of leveraging the MXNet ecosystem, the GluonCV/NLP documentation is supplemented by the interactive open source book Dive into Deep Learning (based on the Gluon API of MXNet) BIBREF7 , which provides sufficient background knowledge about GluonCV/NLP tasks, models, and building blocks. Notably, some users of Dive into Deep Learning have later become contributors of GluonCV/NLP. Requirement, Availability, and Community GluonCV/NLP are implemented in Python and are available for systems running Linux, macOS, and Windows since Python is platform agnostic. The minimum and open source package (e.g., MXNet) requirements are specified in the documentation. As of the time of writing, GluonCV/NLP have reached version 0.6 and 0.4 respectively, and have been open sourced under the Apache 2.0 license. Since the initial release of the source code in April 2018, GluonCV/NLP have attracted 100 contributors worldwide. Models of GluonCV/NLP have been downloaded for more than 1.6 million times in fewer than 10 months. Performance We demonstrate the performance of GluonCV/NLP models in various computer vision and natural language processing tasks. Specifically, we evaluate popular or state-of-the-art models on standard benchmark data sets. In the experiments, we compare model performance between GluonCV/NLP and other open source implementations with Caffe, Caffe2, Theano, and TensorFlow, including ResNet BIBREF8 and MobileNet BIBREF9 for image classification (ImageNet), Faster R-CNN BIBREF10 for object detection (COCO), Mask R-CNN BIBREF11 for instance segmentation, Simple Pose BIBREF12 for pose estimation (COCO), textCNN BIBREF13 for sentiment analysis (TREC), and BERT BIBREF14 for question answering (SQuAD 1.1), sentiment analysis (SST-2), natural langauge inference (MNLI-m), and paraphrasing (MRPC). Table TABREF5 shows that the GluonCV/GluonNLP implementation matches or outperforms the compared open source implementation for the same model evaluated on the same data set. Conclusion GluonCV/NLP provide modular APIs and the model zoo to allow users to rapidly try out new ideas or develop downstream applications in computer vision and natural language processing. GluonCV/NLP are in active development and our future works include further enriching the API and the model zoo, and supporting deployment in more scenarios. We would like to thank all the contributors of GluonCV and GluonNLP (the git log command can be used to list all the contributors). Specifically, we thank Xiaoting He, Heewon Jeon, Kangjian Wu, and Luyu Xia for providing part of results in Table TABREF5 . We would also like to thank the entire MXNet community for their foundational contributions.
Do they experiment with the toolkits?
5f2bade0881c719ab026bc2e2962e2ada96cdb25
Do Ġthey Ġexperiment Ġwith Ġthe Ġtool k its ?
Yes
<s> Do Ġthey Ġexperiment Ġwith Ġthe Ġtool k its ? </s> Introduction </s> Deep Ġlearning , Ġa Ġsub - field Ġof Ġmachine Ġlearning Ġresearch , Ġhas Ġdriven Ġthe Ġrapid Ġprogress Ġin Ġartificial Ġintelligence Ġresearch , Ġleading Ġto Ġastonishing Ġbreakthrough s Ġon Ġlong - standing Ġproblems Ġin Ġa Ġplethora Ġof Ġfields Ġsuch Ġas Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing . ĠTools Ġpowered Ġby Ġdeep Ġlearning Ġare Ġchanging Ġthe Ġway Ġmovies Ġare Ġmade , Ġdiseases Ġare Ġdiagnosed , Ġand Ġplay Ġa Ġgrowing Ġrole Ġin Ġunderstanding Ġand Ġcommunicating Ġwith Ġhumans . </s> Such Ġdevelopment Ġis Ġmade Ġpossible Ġby Ġdeep Ġlearning Ġframeworks , Ġsuch Ġas ĠC affe ĠB IB REF 0 Ġ, ĠCh ainer ĠB IB REF 1 Ġ, ĠC NT K ĠB IB REF 2 Ġ, ĠApache Ġ( inc ub ating ) ĠMX Net ĠB IB REF 3 Ġ, ĠPy Tor ch ĠB IB REF 4 Ġ, ĠT ensor Flow ĠB IB REF 5 Ġ, Ġand ĠThe ano ĠB IB REF 6 Ġ. ĠThese Ġframeworks Ġhave Ġbeen Ġcrucial Ġin Ġdissemin ating Ġideas Ġin Ġthe Ġfield . ĠSpecifically , Ġimperative Ġtools , Ġarguably Ġspearheaded Ġby ĠCh ainer , Ġare Ġeasy Ġto Ġlearn , Ġread , Ġand Ġdebug . ĠSuch Ġbenefits Ġmake Ġimperative Ġprogramming Ġinterface Ġquickly Ġadopted Ġby Ġthe ĠG lu on ĠAPI Ġof ĠMX Net Ġ( while Ġcan Ġbe Ġseamlessly Ġswitched Ġto Ġsymbolic Ġprogramming Ġfor Ġhigh Ġperformance ), ĠPy Tor ch , Ġand ĠT ensor Flow ĠE ager . </s> L ever aging Ġthe Ġimperative ĠG lu on ĠAPI Ġof ĠMX Net , Ġwe Ġdesign Ġand Ġdevelop Ġthe ĠG lu on CV Ġand ĠG lu on N LP Ġ( re ferred Ġto Ġas ĠG lu on CV / N LP Ġhere ina fter ) Ġtool k its Ġfor Ġdeep Ġlearning Ġin Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing . ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , ĠG lu on CV / N LP Ġare Ġthe Ġfirst Ġopen Ġsource Ġtool k its Ġfor Ġdeep Ġlearning Ġin Ġboth Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing Ġthat Ġsimultaneously Ġi ) Ġprovide Ġmodular ĠAPIs Ġto Ġallow Ġcustomization Ġby Ġre - using Ġefficient Ġbuilding Ġblocks ; Ġii ) Ġprovide Ġpre - trained Ġstate - of - the - art Ġmodels , Ġtraining Ġscripts , Ġand Ġtraining Ġlogs Ġto Ġenable Ġfast Ġprototyp ing Ġand Ġpromote Ġreprodu cible Ġresearch ; Ġiii ) Ġleverage Ġthe ĠMX Net Ġecosystem Ġso Ġthat Ġmodels Ġcan Ġbe Ġdeployed Ġin Ġa Ġwide Ġvariety Ġof Ġprogramming Ġlanguages Ġincluding ĠC ++ , ĠClojure , ĠJava , ĠJulia , ĠPerl , ĠPython , ĠR , Ġand ĠScala . </s> Design Ġand ĠFeatures </s> In Ġthe Ġfollowing , Ġwe Ġdescribe Ġthe Ġdesign Ġand Ġfeatures Ġof ĠG lu on CV / N LP . </s> Mod ular ĠAPIs </s> G lu on CV / N LP Ġprovide Ġaccess Ġto Ġmodular ĠAPIs Ġto Ġallow Ġusers Ġto Ġcustomize Ġtheir Ġmodel Ġdesign , Ġtraining , Ġand Ġinference Ġby Ġre - using Ġefficient Ġcomponents Ġacross Ġdifferent Ġmodels . ĠSuch Ġcommon Ġcomponents Ġinclude Ġ( but Ġare Ġnot Ġlimited Ġto ) Ġdata Ġprocessing Ġutilities , Ġmodels Ġwith Ġindividual Ġcomponents , Ġinitialization Ġmethods , Ġand Ġloss Ġfunctions . </s> To Ġeluc idate Ġhow Ġthe Ġmodular ĠAPI Ġfacilitates Ġefficient Ġimplementation , Ġlet Ġus Ġtake Ġthe Ġdata ĠAPI Ġof ĠG lu on CV / N LP Ġas Ġan Ġexample , Ġwhich Ġis Ġused Ġto Ġbuild Ġefficient Ġdata Ġpipelines Ġwith Ġpopular Ġbenchmark Ġdata Ġsets Ġor Ġthose Ġsupplied Ġby Ġusers . ĠIn Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing Ġtasks , Ġinputs Ġor Ġlabels Ġoften Ġcome Ġin Ġwith Ġdifferent Ġshapes , Ġsuch Ġas Ġimages Ġwith Ġa Ġvarying Ġnumber Ġof Ġobjects Ġand Ġsentences Ġof Ġdifferent Ġlengths . ĠThus , Ġthe Ġdata ĠAPI Ġprovides Ġa Ġcollection Ġof Ġutilities Ġto Ġsample Ġinputs Ġor Ġlabels Ġthen Ġtransform Ġthem Ġinto Ġmini - bat ches Ġto Ġbe Ġefficiently Ġcomputed . ĠBesides , Ġusers Ġcan Ġaccess Ġa Ġwide Ġrange Ġof Ġpopular Ġdata Ġsets Ġvia Ġthe Ġdata ĠAPI , Ġincluding Ġ( but Ġare Ġnot Ġlimited Ġto ) ĠImage Net Ġof Ġimage Ġclassification , ĠV OC Ġof Ġobject Ġdetection , ĠC OC O Ġof Ġinstance Ġsegment ation , ĠS Qu AD Ġof Ġquestion Ġanswering , Ġand ĠS ST Ġof Ġsentiment Ġanalysis . </s> Model ĠZoo </s> Building Ġupon Ġthose Ġmodular ĠAPIs , ĠG lu on CV / N LP Ġprovide Ġpre - trained Ġstate - of - the - art Ġmodels , Ġtraining Ġscripts , Ġand Ġtraining Ġlogs Ġvia Ġthe Ġmodel Ġzoo Ġto Ġenable Ġfast Ġprototyp ing Ġand Ġpromote Ġreprodu cible Ġresearch . ĠAs Ġof Ġthe Ġtime Ġof Ġwriting , ĠG lu on CV / N LP Ġhave Ġprovided Ġover Ġ100 Ġmodels Ġfor Ġcommon Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing Ġtasks , Ġsuch Ġas Ġimage Ġclassification , Ġobject Ġdetection , Ġsemantic Ġsegment ation , Ġinstance Ġsegment ation , Ġpose Ġestimation , Ġword Ġembed ding , Ġlanguage Ġmodel , Ġmachine Ġtranslation , Ġsentiment Ġanalysis , Ġnatural Ġlanguage Ġinference , Ġdependency Ġparsing , Ġand Ġquestion Ġanswering . </s> L ever aging Ġthe ĠMX Net ĠE cosystem </s> G lu on CV / N LP Ġhave Ġbenef itted Ġfrom Ġthe ĠMX Net Ġecosystem Ġthrough Ġuse Ġof ĠMX Net . ĠAt Ġthe Ġlowest Ġlevel , ĠMX Net Ġprovides Ġhigh - performance ĠC ++ Ġimplementations Ġof Ġoperators Ġthat Ġare Ġlever aged Ġby ĠG lu on CV / N LP ; Ġthus , Ġimprovements Ġin Ġlow - level Ġcomponents Ġof ĠMX Net Ġoften Ġresult Ġin Ġperformance Ġgains Ġin ĠG lu on CV / N LP . ĠSame Ġas Ġany Ġother Ġmodel Ġimplemented Ġwith ĠMX Net , ĠG lu on CV / N LP Ġcan Ġbe Ġused Ġto Ġtrain Ġmodels Ġon ĠCPU , ĠGPU Ġ( single Ġor Ġmultiple ), Ġand Ġmultiple Ġmachines . ĠIn Ġsharp Ġcontrast Ġto Ġbuilding Ġupon Ġother Ġdeep Ġlearning Ġframeworks , Ġthrough Ġthe Ġunique Ġhybrid izing Ġmechanism Ġby ĠMX Net ĠB IB REF 7 Ġ, Ġusually ĠG lu on CV / N LP Ġmodels Ġcan Ġbe Ġdeployed Ġwith Ġno Ġor Ġminimal Ġconfiguration Ġin Ġa Ġwide Ġspectrum Ġof Ġprogramming Ġlanguages Ġincluding ĠC ++ , ĠClojure , ĠJava , ĠJulia , ĠPerl , ĠPython , ĠR , Ġand ĠScala . ĠThere Ġare Ġalso Ġongoing Ġefforts Ġto Ġbring Ġmore Ġquant ization Ġ( int 8 Ġand Ġfloat 16 Ġinference ) Ġbenefits Ġfrom ĠMX Net Ġto ĠG lu on CV / N LP Ġto Ġfurther Ġaccelerate Ġmodel Ġinference . </s> The Ġdocumentation Ġhttps :// g lu on - cv . mx net . io / Ġand Ġhttp :// g lu on - nl p . mx net . io / Ġof ĠG lu on CV / N LP Ġinclude Ġinstallation Ġinstructions , Ġcontribution Ġinstructions , Ġopen Ġsource Ġrepositories , Ġextensive ĠAPI Ġreference , Ġand Ġcomprehensive Ġtutorials . ĠAs Ġanother Ġbenefit Ġof Ġleveraging Ġthe ĠMX Net Ġecosystem , Ġthe ĠG lu on CV / N LP Ġdocumentation Ġis Ġsupplemented Ġby Ġthe Ġinteractive Ġopen Ġsource Ġbook ĠDive Ġinto ĠDeep ĠLearning Ġ( based Ġon Ġthe ĠG lu on ĠAPI Ġof ĠMX Net ) ĠB IB REF 7 Ġ, Ġwhich Ġprovides Ġsufficient Ġbackground Ġknowledge Ġabout ĠG lu on CV / N LP Ġtasks , Ġmodels , Ġand Ġbuilding Ġblocks . ĠNotably , Ġsome Ġusers Ġof ĠDive Ġinto ĠDeep ĠLearning Ġhave Ġlater Ġbecome Ġcontributors Ġof ĠG lu on CV / N LP . </s> Requ irement , ĠAvailability , Ġand ĠCommunity </s> G lu on CV / N LP Ġare Ġimplemented Ġin ĠPython Ġand Ġare Ġavailable Ġfor Ġsystems Ġrunning ĠLinux , ĠmacOS , Ġand ĠWindows Ġsince ĠPython Ġis Ġplatform Ġag nostic . ĠThe Ġminimum Ġand Ġopen Ġsource Ġpackage Ġ( e . g ., ĠMX Net ) Ġrequirements Ġare Ġspecified Ġin Ġthe Ġdocumentation . ĠAs Ġof Ġthe Ġtime Ġof Ġwriting , ĠG lu on CV / N LP Ġhave Ġreached Ġversion Ġ0 . 6 Ġand Ġ0 . 4 Ġrespectively , Ġand Ġhave Ġbeen Ġopen Ġsourced Ġunder Ġthe ĠApache Ġ2 . 0 Ġlicense . ĠSince Ġthe Ġinitial Ġrelease Ġof Ġthe Ġsource Ġcode Ġin ĠApril Ġ2018 , ĠG lu on CV / N LP Ġhave Ġattracted Ġ100 Ġcontributors Ġworldwide . ĠModels Ġof ĠG lu on CV / N LP Ġhave Ġbeen Ġdownloaded Ġfor Ġmore Ġthan Ġ1 . 6 Ġmillion Ġtimes Ġin Ġfewer Ġthan Ġ10 Ġmonths . </s> Performance </s> We Ġdemonstrate Ġthe Ġperformance Ġof ĠG lu on CV / N LP Ġmodels Ġin Ġvarious Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing Ġtasks . ĠSpecifically , Ġwe Ġevaluate Ġpopular Ġor Ġstate - of - the - art Ġmodels Ġon Ġstandard Ġbenchmark Ġdata Ġsets . ĠIn Ġthe Ġexperiments , Ġwe Ġcompare Ġmodel Ġperformance Ġbetween ĠG lu on CV / N LP Ġand Ġother Ġopen Ġsource Ġimplementations Ġwith ĠC affe , ĠC affe 2 , ĠThe ano , Ġand ĠT ensor Flow , Ġincluding ĠRes Net ĠB IB REF 8 Ġand ĠMobile Net ĠB IB REF 9 Ġfor Ġimage Ġclassification Ġ( Image Net ), ĠFaster ĠR - CNN ĠB IB REF 10 Ġfor Ġobject Ġdetection Ġ( C OC O ), ĠMask ĠR - CNN ĠB IB REF 11 Ġfor Ġinstance Ġsegment ation , ĠSimple ĠPose ĠB IB REF 12 Ġfor Ġpose Ġestimation Ġ( C OC O ), Ġtext CNN ĠB IB REF 13 Ġfor Ġsentiment Ġanalysis Ġ( T REC ), Ġand ĠB ERT ĠB IB REF 14 Ġfor Ġquestion Ġanswering Ġ( S Qu AD Ġ1 . 1 ), Ġsentiment Ġanalysis Ġ( S ST - 2 ), Ġnatural Ġlang au ge Ġinference Ġ( MN LI - m ), Ġand Ġparaph r asing Ġ( MR PC ). ĠTable ĠT AB REF 5 Ġshows Ġthat Ġthe ĠG lu on CV / G lu on N LP Ġimplementation Ġmatches Ġor Ġoutper forms Ġthe Ġcompared Ġopen Ġsource Ġimplementation Ġfor Ġthe Ġsame Ġmodel Ġevaluated Ġon Ġthe Ġsame Ġdata Ġset . </s> Conclusion </s> G lu on CV / N LP Ġprovide Ġmodular ĠAPIs Ġand Ġthe Ġmodel Ġzoo Ġto Ġallow Ġusers Ġto Ġrapidly Ġtry Ġout Ġnew Ġideas Ġor Ġdevelop Ġdownstream Ġapplications Ġin Ġcomputer Ġvision Ġand Ġnatural Ġlanguage Ġprocessing . ĠG lu on CV / N LP Ġare Ġin Ġactive Ġdevelopment Ġand Ġour Ġfuture Ġworks Ġinclude Ġfurther Ġenrich ing Ġthe ĠAPI Ġand Ġthe Ġmodel Ġzoo , Ġand Ġsupporting Ġdeployment Ġin Ġmore Ġscenarios . </s> We Ġwould Ġlike Ġto Ġthank Ġall Ġthe Ġcontributors Ġof ĠG lu on CV Ġand ĠG lu on N LP Ġ( the Ġgit Ġlog Ġcommand Ġcan Ġbe Ġused Ġto Ġlist Ġall Ġthe Ġcontributors ). ĠSpecifically , Ġwe Ġthank ĠXia oting ĠHe , ĠHe ew on ĠJe on , ĠKang j ian ĠWu , Ġand ĠL uy u ĠXia Ġfor Ġproviding Ġpart Ġof Ġresults Ġin ĠTable ĠT AB REF 5 Ġ. ĠWe Ġwould Ġalso Ġlike Ġto Ġthank Ġthe Ġentire ĠMX Net Ġcommunity Ġfor Ġtheir Ġfoundational Ġcontributions .