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AWS Rekognition: Rich Image Metadata Extraction Powered by Deep Learning
AWS Rekognition is a deep learning service that allows customers to extract rich metadata like objects, scenes, faces, and moderation labels from images. It uses convolutional neural networks trained on large datasets to perform tasks like detecting 80+ object labels, facial analysis, comparisons, and celebrity recognition. Rekognition also offers image moderation to detect unsafe or unwanted content.
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AWS Rekognition: Rich Image Metadata Extraction Powered by Deep Learning
- 1. © 2017, AmazonWeb Services, Inc. or its Affiliates. All rights reserved. AWS Rekognition Rich Image Metadata Extraction Powered by Deep Learning Adrian Hornsby, Technical Evangelist @adhorn adhorn@amazon.com
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- 7. Deep Learning Process Conv1 Conv 2 Conv n … … Feature Maps Labrador Dog Beach Outdoors Softmax Probability Fully Connected Layer
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- 9. The Challenge ForArtificial Intelligence: SCALE Training Tons of GPUs
- 10. Data The Challenge ForArtificial Intelligence: SCALE PBs of existing data Training Tons of GPUs
- 11. Prediction The Challenge ForArtificial Intelligence: SCALE Tons of GPUs and CPUs Data PBs of existing data Training Tons of GPUs
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- 13. Amazon Rekognition Object and scenedetection Facial analysis Face comparison Celebrity recognition Image moderation Amazon Rekognition: Images In, Rich Metadata Out
- 14. Object & SceneDetection
- 15. Object & SceneDetection
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- 22. Image moderation Explicit Nudity Nudity GraphicMale Nudity Graphic Female Nudity Sexual Activity Partial Nudity Suggestive Female Swimwear or Underwear Male Swimwear or Underwear Revealing Clothes
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- 24. Amazon Rekognition Amazon Rekognition: supportedactions CompareFaces CreateCollection DeleteCollection DeleteFaces DetectFaces DetectLabels DetectModerationLabels GetCelebrityInfo IndexFaces ListCollections ListFaces RecognizeCelebrities SearchFaces SearchFacesByImage
- 25. Collection IndexFaces SearchFacesbyImage Nearest neighbor search FaceID: 4c55926e-69b3-5c80-8c9b-78ea01d30690 Similarity:97 FaceID: 02e56305-1579-5b39-ba57-9afb0fd8782d Similarity: 92 FaceID: 02e56305-1579-5b39-ba57-9afb0fd8782d Similarity: 85
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- 29. Amazon Rekognition Customers • DigitalAsset Management • Media and Entertainment • Travel and Hospitality • Influencer Marketing • Systems Integration • Digital Advertising • Consumer Storage • Law Enforcement • Public Safety • eCommerce • Education
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- 34. Amazon Rekognition Pricing FreeTier: 5000 images processed per month for first 12 months Image Analysis Tiers Price per 1000 images processed First 1 million images processed* per month $1.00 Next 9 million images processed* per month $0.80 Next 90 million images processed* per month $0.60 Over 100 million images processed* per month $0.40 Face Metadata Storage Price per 1,000 face metadata stored per month Face metadata stored $0.01
Editor's Notes
- #3 Today Ai is everywhere is Amazon From recommendation pages, to fulfillment centers, Prime Air Drones, Amazon Go and Alexa
- #7 Such systems can be trained from examples, rather than explicitly specified. They have found most use in applications difficult to express in a traditional computer algorithm using ordinary rule-based programming. Neural networks have been used to solve a wide variety of tasks, including computer vision, speech recognition, machine translation and medical diagnosis.
- #8 Deep learning is part of a broader family of machine learning methods based on learning data representations. An observation (e.g., an image) can be represented in many ways, eg., as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations are better than others at simplifying a specific task. A deep neural network (DNN) is an artificial neural network (ANN) with multiple hidden layers between the input and output layers.[4][6] Similar to shallow ANNs, DNNs can model complex non-linear relationships. DNN architectures generate compositional models where the object is expressed as a layered composition of image primitives.[106] The extra layers enable composition of features from lower layers, giving the potential of modeling complex data with fewer units than a similarly performing shallow network.[4]
- #9 To achieve accurate results on complex Computer Vision tasks such as object and scene detection, face analysis, and face recognition, deep learning systems need to be tuned properly and trained with massive amounts of labeled ground truth data. Sourcing, cleaning, and labeling data accurately is a time-consuming and expensive task. Moreover, training a deep neural network is computationally expensive and often requires custom hardware built using Graphics Processing Units (GPU).
- #10 And for us, AI is a system or service which can perform tasks that usually require human intelligence, such as visual perception, speech recognition, decision-making or translation
- #11 So this includes machine learning and deep learning.
- #12 The training phase and its self learning feature are well adapted to solving informal problems, but here’s the catch: they involve a lot of math operations which tend to grow exponentially as data size increases and as the number of layers increases. This problem is called the “Curse of Dimensionality” and it’s one of the major reasons why neural networks stagnated for decades: there was simply not enough computing power available to run them at scale.
- #13 Nor was enough data available. Neural networks need a lot of data to learn properly. The more data, the better! Until recently, it was simply not possible to gather and store vast amounts of digital data.
- #14 Finally, running the models require a tones of GPUs and CPUs. With the advances in cloud computing, all those conditions are now met – planets are finally alligned for AI to become a reality.
- #15 Amazon Web Services provides a rich ecosystem to help you build smarter applications. In this context, it is worth highlighting the higher level AI services based on deep learning algorithms, like Amazon Rekognition, an image recognition service, Amazon Polly, a text to speech synthesizer, and Amazon Lex, a voice and text chatbot service. We also provide the infrastructure including GPU EC2 instances for fast parallel processing which you can use in combination with any of the popular deep learning libraries like Apache mxnet, Tensorflow, Theano, etc, all of which are available on the AWS deep learning AMI. For your general machine learning purposes, you can also use EC2, Amazon Elastic MapReduce and Spark with SparkML to run any machine learning algorithm. Another popular library is the python scikit-learn, which you can deploy on AWS Lambda or containers, or EC2. So what I am trying to convey is that there is a lot of choice, which basically boils down to picking the right tool for the right job, where you can make trade-offs between ‘do your own’ with all the flexibility, or picking a managed solution which allows you to get results fast without having to do the heavy lifting.
- #16 Amazon Rekognition currently supports the JPEG and PNG image formats. You can submit images either as an S3 object or as a byte array. Amazon Rekognition supports image file sizes up to 15MB when passed as an S3 object, and up to 5MB when submitted as an image byte array. Amazon Rekognition is currently available in US East (Northern Virginia), US West (Oregon) and EU (Ireland) regions. Mxnet convolutional deep neural networks (CNNs),
- #20 Image moderation Rekognition automatically detects explicit or suggestive adult content in your images, and provides confidence scores.
- #25 You can use the ‘MinConfidence’ parameter in your API requests to balance detection of content (recall) vs the accuracy of detection (precision).
- #26 Explicit Nudity Nudity Graphic Male Nudity Graphic Female Nudity Sexual Activity Partial Nudity Suggestive Female Swimwear or Underwear Male Swimwear or Underwear Revealing Clothes
- #27 Amazon Rekognition currently supports the JPEG and PNG image formats. You can submit images either as an S3 object or as a byte array. Amazon Rekognition supports image file sizes up to 15MB when passed as an S3 object, and up to 5MB when submitted as an image byte array. Amazon Rekognition is currently available in US East (Northern Virginia), US West (Oregon) and EU (Ireland) regions.
- #33 The C-SPAN Archives is responsible for indexing speakers in C-SPAN video content. In the past, this manual process was performed by a team of two to three indexers. The initial image collection—consisting of 97,000 known individuals—was indexed by Amazon Rekognition in less than two hours.
- #34 "By using Amazon Rekognition's advanced facial recognition and metadata analysis, we are able to provide more detailed influencer audience metrics to our growing list of customers such as Bell Media, L’Oreal, Samsung, 20th Century Fox, Walmart and many more."
- #35 Moderation: To determine if an image contains explicit or suggestive adult content
- #37 Democratizing Image Analysis (key factors: affordable, accessible, scalable) that helps you get started in minutes