The document discusses GluonCV, an open source computer vision toolkit for Apache MXNet and Gluon. GluonCV provides state-of-the-art pre-trained models for classification, detection, segmentation, and other tasks. It offers easy and fast development of vision models as well as deployment of trained models. Examples of pre-trained models and their accuracy are shown for classification, detection, and segmentation tasks. Information on getting started with GluonCV, MXNet, and Gluon NLP is also provided.

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Building State-of-the-Art
Computer Vision Models Using
Apache MXNet and Gluon
Tong He
Applied Scientist
Amazon AWS
A I M 3 6 1
Marcelo Bossi
Technical Business Developer
Amazon AWS

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GluonCV: A vision toolkit
• State-of-the-art models
• Fast development
• Easy deployment
• Official maintenance

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One of the best open source choices
• Pre-trained models with great accuracy
• Comprehensive Model Zoo

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Models
• Classification • Detection • Segmentation

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Models
• Classification
Model Ours Reference
ResNet-50 79.2% 76.2%
ResNet-101 80.5% 77.4%
MobileNet 73.3% 70.9%

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Models
• Detection
Model Ours Reference
Faster-RCNN 40.1% 39.6%
YOLOv3 37.0% 33.0%

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Models
• Segmentation
Model Ours Reference
Mask-RCNN 33.1% 32.8%
DeepLab-v3 86.7% 85.7%

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Models
• Available
• Classification
• Detection
• Segmentation
• Re-ID
• GAN
• In development
• Keypoint detection
• Depth prediction

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Getting started
• Apache MXNet: http://beta.mxnet.io
• Gluon CV: https://gluon-cv.mxnet.io
• Gluon NLP: https://gluon-nlp.mxnet.io
• Apache MXNet on AWS: https://aws.amazon.com/mxnet
• Contact: mxnet-info@amazon.com

11. Thank you!
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Appendix

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Keras - MXNet
• Try it out now: https://github.com/awslabs/keras-apache-mxnet
3x throughput on training 1.5x throughput on inference

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Applications: Autonomous vehicles
• TuSimple, a leader in self-driving technology,
uses Apache MXNet to build sophisticated
deep-learning algorithms for computer vision
and driving simulation
• Relies on MXNet to teach computers how to
recognize and track objects and make
decisions to avoid collisions and prioritize
safety
• Simulated a billion miles of road driving with
a wide range of variables and driving
conditions – the largest simulation of its kind
in history
• Used NVIDIA GPUs, NVIDIA DRIVE PX
2, Jetson TX2, CUDA, TensorRT, and cuDNN
to develop its autonomous driving solution

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Applications: Tackle phishing attacks
• Pixm uses a deep-learning computer-vision-
based endpoint security solution to detect
phishing attacks in real time at the point of a
click, within your browser, either on a desktop or
a laptop
• MXNet was chosen because it has some of the
best support for multiple OSs and high-
performance model inference, and allows
rapid-scale training on large volumes of image
data using GPUs available on Amazon Elastic
Compute Cloud (Amazon EC2) P3 instances
• By using deep-learning computer vision for
real-time detection of phishing websites, Pixm
helps protect clients from major cyberattacks
and the costs associated with them, which can
amount to millions of dollars and loss of
customer trust

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Applications: Convert social influence into sales
• Curalate uses deep learning to make products
more discoverable on social media, which
translates into increased revenue for brands
and retailers
• Curalate has an intelligent product tagging
service that is trained using deep-learning
algorithms, developed with the Apache
MXNet framework, so brands can match
images with products to recommend to
shoppers
• Using MXNet allows Curlate to quickly build,
train, and deploy production models on AWS
• MXNet’s support for Scala APIs streamlines
the development process and will help
Curalate scale up its social commerce
platform

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MXNet Customer Momentum