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Host multiple TensorFlow computer vision models using Amazon SageMaker multi-model endpoints

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Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML. SageMaker accelerates innovation within your organization by providing purpose-built tools for every step of ML development, including labeling, data preparation, feature engineering, statistical bias detection, AutoML, training, tuning, hosting, explainability, monitoring, and workflow automation. Companies are increasingly training ML models based on individual user data. For example, an image sharing service designed to enable discovery of information on the internet trains custom models based on each user's uploaded images and browsing history to personalize recommendations for that user. The company can also train custom models based on search topics for recommending images per topic.


Interpretable Distance Metric Learning for Handwritten Chinese Character Recognition

arXiv.org Artificial Intelligence

Handwriting recognition is of crucial importance to both Human Computer Interaction (HCI) and paperwork digitization. In the general field of Optical Character Recognition (OCR), handwritten Chinese character recognition faces tremendous challenges due to the enormously large character sets and the amazing diversity of writing styles. Learning an appropriate distance metric to measure the difference between data inputs is the foundation of accurate handwritten character recognition. Existing distance metric learning approaches either produce unacceptable error rates, or provide little interpretability in the results. In this paper, we propose an interpretable distance metric learning approach for handwritten Chinese character recognition. The learned metric is a linear combination of intelligible base metrics, and thus provides meaningful insights to ordinary users. Our experimental results on a benchmark dataset demonstrate the superior efficiency, accuracy and interpretability of our proposed approach.


Computer Vision: Python OCR & Object Detection Quick Starter

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Online Courses Udemy - Computer Vision: Python OCR & Object Detection Quick Starter, Quick Starter for Optical Character Recognition, Image Recognition Object Detection and Object Recognition using Python Hot & New Created by Abhilash Nelson English Students also bought Python 3.8 for beginners 2020 Docker for Beginners Python Programming from Basics to Advanced FL Studio 20 - EDM Masterclass Music Production in FL Studio Microsoft Azure Data Lake Storage Service (Gen1 & Gen2) Geospatial Data Analyses & Remote Sensing: 4 Classes in 1 Preview this course GET COUPON CODE Description Hi There! welcome to my new course'Optical Character Recognition and Object Recognition Quick Start with Python'. This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document.


One-Shot learning based classification for segregation of plastic waste

arXiv.org Artificial Intelligence

The problem of segregating recyclable waste is fairly daunting for many countries. This article presents an approach for image based classification of plastic waste using one-shot learning techniques. The proposed approach exploits discriminative features generated via the siamese and triplet loss convolutional neural networks to help differentiate between 5 types of plastic waste based on their resin codes. The approach achieves an accuracy of 99.74% on the WaDaBa Database


Computer Vision: Python OCR & Object Detection Quick Starter

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This is the third course from my Computer Vision series. Image Recognition, Object Detection, Object Recognition and also Optical Character Recognition are among the most used applications of Computer Vision. Using these techniques, the computer will be able to recognize and classify either the whole image, or multiple objects inside a single image predicting the class of the objects with the percentage accuracy score. Using OCR, it can also recognize and convert text in the images to machine readable format like text or a document. Object Detection and Object Recognition is widely used in many simple applications and also complex ones like self driving cars.


Accelerate reverse image search with GPU for feature extraction

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In this code pattern, work through the process of analyzing an image data set using a pre-trained convolution network (VGG16) and extracting feature vectors for each image using a Jupyter Notebook. Machine learning algorithms provide many useful tools that solve real-world problems. One of the domains that machine learning has had great success with is image recognition. By using computational power to identify images and compare them to other images, you can use machines to perform tasks that a few years ago could be done only by humans. Engineers and data scientists who work with image recognition can encounter a few challenges that can put limits on the work that can be done with machine learning algorithms.


Nvidia GANimal uses A.I. To Make Animals Smile in Pictures Digital Trends

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Can't get your dog or that tiger at the zoo to smile for your Instagram? A new artificially intelligent program developed by researchers from Nvidia can take the expression from one animal and put it on the photo of another animal. Called GANimal -- after generative adversarial networks, a type of A.I. -- the software allows users to upload an image of one animal to re-create the pet's expression and pose on another animal. GAN programs are designed to convert one image to look like another, but are typically focused on more narrow tasks like turning horses to zebras. GANimal, however, applies several different changes to the image, adjusting the expression, the position of the animal's head, and in many cases, even the background, from the inspiration image onto the source image.


Integrating Deep Learning With clojure.spec - Squid's Blog

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It also provides a generative aspect that allows for robust testing as well as an additional way to understand your data through manual inspection. The dual nature of validation and generation is a natural fit for deep learning models that consist of paired discriminator/generator models. TLDR: In this post we show that you can leverage the dual nature of clojure.spec's A common use of clojure.spec is at the boundaries to validate that incoming data is indeed in the expected form. Again, this is boundary is a fitting place to integrate models for the deep learning paradigm and our traditional software code.



Applications of Zero-Shot Learning

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As a member of a research group involved in computer vision, I wanted to write this short article to briefly present what we call "Zero-shot learning" (ZSL), an interesting variant of transfer learning, and the current research related to it. Today, many machine learning methods focus on classifying instances whose classes have already been seen in training. Concretely, many applications require classifying instances whose classes have not been seen before. Zero-shot learning is a promising learning method, in which the classes covered by training instances and the classes we aim to classify are disjoint. In other words, Zero-shot learning is about leveraging supervised learning with no additional training data.