This allows you to focus on high-value application design and development. Amazon Rekognition continues to improve the accuracy of its models by building upon the latest research and sourcing new training data.
Moreover, training a deep neural network is computationally expensive and often requires custom hardware built using Graphics Processing Units (GPU).Īmazon Rekognition is fully managed and comes pre-trained for image and video recognition tasks, so that you don’t have invest your time and resources on creating a deep learning pipeline. Sourcing, cleaning, and labeling data accurately is a time-consuming and expensive task. 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.
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With Amazon Rekognition, you don’t have to build, maintain or upgrade deep learning pipelines. Q: Do I need any deep learning expertise to use Amazon Rekognition? Amazon AI services use deep learning to understand images, turn text into lifelike speech, and build intuitive conversational text and speech interfaces. Several deep learning architectures such as convolutional deep neural networks (CNNs), and recurrent neural networks have been applied to computer vision, speech recognition, natural language processing, and audio recognition to produce state-of-the-art results on various tasks.Īmazon Rekognition is a part of the Amazon AI family of services. Learning occurs by iteratively estimating hundreds of thousands of parameters in the deep graph with efficient algorithms. Deep learning replaces handcrafted features with ones learned from very large amounts of annotated data. Deep learning is loosely based on models of information processing and communication in the brain. It aims to infer high-level abstractions from raw data by using a deep graph with multiple processing layers composed of multiple linear and non-linear transformations. Rekognition Video allows you also to index metadata like objects, activities, scene, celebrities, and faces that make video search easy.ĭeep learning is a sub-field of Machine Learning and a significant branch of Artificial Intelligence. For example, this could be used in an application that sends a real-time notification when someone delivers a package to your door. Rekognition Video detects persons and tracks them through the video even when their faces are not visible, or as the whole person might go in and out of the scene. Rekognition Video is a video recognition service that detects activities understands the movement of people in frame and recognizes objects, celebrities, and inappropriate content in videos stored in Amazon S3 and live video streams from Acuity. With Rekognition Image, you only pay for the images you analyze and the face metadata you store. Rekognition Image uses deep neural network models to detect and label thousands of objects and scenes in your images, and we are continually adding new labels and facial recognition features to the service. It also allows you to search and compare faces. Rekognition Image is an image recognition service that detects objects, scenes, and faces extracts text recognizes celebrities and identifies inappropriate content in images. Rekognition Video lets you extract motion-based context from stored or live stream videos and helps you analyze them. Rekognition Image lets you easily build powerful applications to search, verify, and organize millions of images. Amazon Rekognition is a service that makes it easy to add powerful visual analysis to your applications.
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