Best Accord.NET Framework Alternatives in 2024
Find the top alternatives to Accord.NET Framework currently available. Compare ratings, reviews, pricing, and features of Accord.NET Framework alternatives in 2024. Slashdot lists the best Accord.NET Framework alternatives on the market that offer competing products that are similar to Accord.NET Framework. Sort through Accord.NET Framework alternatives below to make the best choice for your needs
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AutoML Vision provides insights from images at the edge and cloud. Pre-trained Vision API models can also be used to understand text and detect emotion. Google Cloud offers two computer vision products, which use machine learning to help understand your images with an industry-leading prediction accuracy. Automate the creation of custom machine learning models. Upload images, train custom image models using AutoML Vision's intuitive graphical interface, optimize your models for accuracy and latency, and export them to your cloud application or to a range of devices at the edge. Google Cloud's Vision API provides powerful pre-trained machine-learning models via REST and RPC APIs. Assign labels to images and classify them quickly into millions of predefined groups. Detect faces and objects, read printed and handwritten texts, and add valuable metadata to your image catalog.
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Azure Computer Vision
Microsoft
By embedding vision capabilities into your apps, you can increase content discovery, automate text extract, analyze video in real-time, and create products that more people will use. Visual data processing can be used to identify content with objects and concepts. It can also generate image descriptions, extract text, and moderate content. It can also be used to understand people's movements in physical spaces. You don't need to have any machine learning skills. -
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Amazon Rekognition
Amazon
Amazon Rekognition allows you to easily add image and video analysis into your applications using proven, highly-scalable, deep learning technology that does not require any machine learning expertise. Amazon Rekognition allows you to identify objects, people and text in images and videos. It also detects inappropriate content. Amazon Rekognition can also be used to perform facial analysis and facial searches. This is useful for many purposes, including user verification, people counting, public safety, and other uses. Amazon Rekognition Custom Labels allow you to identify objects and scenes in images that meet your business requirements. You can create a model to help you classify machine parts or detect plants that are sick. Amazon Rekognition Custom Labels does all the heavy lifting for you. -
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AForge.NET
AForge.NET
AForge.NET is an open-source C# framework for researchers and developers in the fields of Computer Vision, Artificial Intelligence - image processors, neural networks, genetic algorithms and fuzzy logic, as well as machine learning and robotics. The framework's development is ongoing, which means that new features and namespaces are being added constantly. You can track the source repository's log to keep track of its progress or visit the project discussion group to receive the most recent information. The framework comes with many examples of applications that demonstrate how to use it, as well as different libraries and their source. -
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Torch
Torch
Torch is a scientific computing platform that supports machine learning algorithms and has wide support for them. It is simple to use and efficient thanks to a fast scripting language, LuaJIT and an underlying C/CUDA implementation. Torch's goal is to allow you maximum flexibility and speed when building your scientific algorithms, while keeping it simple. Torch includes a large number of community-driven packages for machine learning, signal processing and parallel processing. It also builds on the Lua community. The core of Torch is the popular optimization and neural network libraries. These libraries are easy to use while allowing for maximum flexibility when implementing complex neural networks topologies. You can create arbitrary graphs of neuro networks and parallelize them over CPUs or GPUs in an efficient way. -
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Google Cloud allows you to quickly build your deep learning project. You can quickly prototype your AI applications using Deep Learning Containers. These Docker images are compatible with popular frameworks, optimized for performance, and ready to be deployed. Deep Learning Containers create a consistent environment across Google Cloud Services, making it easy for you to scale in the cloud and shift from on-premises. You can deploy on Google Kubernetes Engine, AI Platform, Cloud Run and Compute Engine as well as Docker Swarm and Kubernetes Engine.
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Insight Toolkit (ITK)
ITK
FreeWelcome to the Insight Toolkit. ITK is an open-source library that allows developers to access a wide range of software tools for image analysis. ITK is based on an established spatially-oriented architecture that allows for segmentation, registration, and processing of scientific images in any number of dimensions. This foundation will allow for reproducible future research. Establish a repository for fundamental algorithms. Create a platform for advanced product design. Commercialization of the technology. Develop conventions for future work. Support education in scientific imaging analysis. Develop a self-sustaining community for software developers and users. ITK is the oldest and largest open-source project in the scientific community. We set out to create a powerful image analysis tool that can be used in a wide range of environments and applications. -
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SHARK
SHARK
SHARK is an open-source C++ machine-learning library that is fast, modular, and feature-rich. It offers methods for linear and unlinear optimization, kernel-based algorithms, neural networks, as well as other machine learning techniques. It is a powerful toolbox that can be used in real-world applications and research. Shark relies on Boost, CMake. It is compatible with Windows and Solaris, MacOS X and Linux. Shark is licensed under the permissive GNU Lesser General Public License. Shark offers a great compromise between flexibility and ease of use and computational efficiency. Shark provides many algorithms from different domains of machine learning and computational intelligence that can be combined and extended easily. Shark contains many powerful algorithms that, to our best knowledge, are not available in any other library. -
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Supervisely
Supervisely
The best platform for the entire lifecycle of computer vision. You can go from image annotation to precise neural networks in 10x less time. Our best-in-class data labeling software transforms images, videos, and 3D point clouds into high-quality training data. You can train your models, track experiments and visualize the results. Our self-hosted solution guarantees data privacy, powerful customization capabilities and easy integration into any technology stack. Computer Vision is a turnkey solution: multi-format data management, quality control at scale, and neural network training in an end-to-end platform. Professional video editing software created by data scientists for data science -- the most powerful tool for machine learning and other purposes. -
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Deeplearning4j
Deeplearning4j
DL4J makes use of the most recent distributed computing frameworks, including Apache Spark and Hadoop, to accelerate training. It performs almost as well as Caffe on multi-GPUs. The libraries are open-source Apache 2.0 and maintained by Konduit and the developer community. Deeplearning4j is written entirely in Java and compatible with any JVM language like Scala, Clojure or Kotlin. The underlying computations are written using C, C++, or Cuda. Keras will be the Python API. Eclipse Deeplearning4j, a commercial-grade, open source, distributed deep-learning library, is available for Java and Scala. DL4J integrates with Apache Spark and Hadoop to bring AI to business environments. It can be used on distributed GPUs or CPUs. When training a deep-learning network, there are many parameters you need to adjust. We have tried to explain them so that Deeplearning4j can be used as a DIY tool by Java, Scala and Clojure programmers. -
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Automaton AI
Automaton AI
Automaton AI's Automaton AI's DNN model and training data management tool, ADVIT, allows you to create, manage, and maintain high-quality models and training data in one place. Automated optimization of data and preparation for each stage of the computer vision pipeline. Automate data labeling and streamline data pipelines in house Automate the management of structured and unstructured video/image/text data and perform automated functions to refine your data before each step in the deep learning pipeline. You can train your own model with accurate data labeling and quality assurance. DNN training requires hyperparameter tuning such as batch size, learning rate, and so on. To improve accuracy, optimize and transfer the learning from trained models. After training, the model can be put into production. ADVIT also does model versioning. Run-time can track model development and accuracy parameters. A pre-trained DNN model can be used to increase the accuracy of your model for auto-labeling. -
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VisionSense
Winjit
Advanced image processing solution and real-time computer vision that uses advanced models of convolutional neuro networks. The product's top applications include building management, identity verification, fraud detection, manufacturing, and quality control. Winjit is a leading technology provider in India with more than ten years of experience in developing engineering solutions across all industries. -
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Pillow
Pillow
FreeThe Python Imaging Library provides image processing capabilities for your Python interpreter. This library supports many file formats, an efficient internal representation, as well as powerful image processing capabilities. The core image library allows for quick access to data stored only in basic pixel formats. It should be a solid foundation for an image processing tool. Tidelift subscribers can get Pillow for Enterprise. The Python Imaging Library is perfect for batch processing and image archival applications. The library can be used to create thumbnails, convert file formats, and print images. The current version can identify and read a wide range of formats. The most common interchange and presentation formats are the only ones that support writing. The library includes basic image processing functionality such as point operations, filtering using a set of convolution kernels and color space conversions. -
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TorchScript allows you to seamlessly switch between graph and eager modes. TorchServe accelerates the path to production. The torch-distributed backend allows for distributed training and performance optimization in production and research. PyTorch is supported by a rich ecosystem of libraries and tools that supports NLP, computer vision, and other areas. PyTorch is well-supported on major cloud platforms, allowing for frictionless development and easy scaling. Select your preferences, then run the install command. Stable is the most current supported and tested version of PyTorch. This version should be compatible with many users. Preview is available for those who want the latest, but not fully tested, and supported 1.10 builds that are generated every night. Please ensure you have met the prerequisites, such as numpy, depending on which package manager you use. Anaconda is our preferred package manager, as it installs all dependencies.
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Caffe
BAIR
Caffe is a deep-learning framework that focuses on expression, speed and modularity. It was developed by Berkeley AI Research (BAIR), and community contributors. The project was created by Yangqing Jia during his PhD at UC Berkeley. Caffe is available under the BSD 2-Clause License. Check out our web image classification demo! Expressive architecture encourages innovation and application. Configuration is all that is required to define models and optimize them. You can switch between CPU and GPU by setting one flag to train on a GPU, then deploy to commodity clusters of mobile devices. Extensible code fosters active development. Caffe was forked by more than 1,000 developers in its first year. Many significant changes were also made back. These contributors helped to track the state of the art in code and models. Caffe's speed makes it ideal for industry deployment and research experiments. Caffe can process more than 60M images per hour using a single NVIDIA GPU K40. -
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LEADTOOLS Imaging Pro
LEADTOOLS
$795 one-time paymentLEADTOOLS Imaging Pro contains all the tools needed to add powerful imaging technology into applications. LEADTOOLS Imaging Pro has more than 32 years experience in imaging development. It includes 150+ image formats and image compression, image processing and image viewers. There are also 200+ image display effects. TWAIN and WIA image scanning and screen capture. LEADTOOLS Image Pro is an entry-level product that allows you to create applications that use LEADTOOLS imaging library. The Pro family includes many additional features, including those in the Document, Recognition and Medical families. The Pro Family also offers the best value in the market for Barcode and PDF. -
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scikit-image
scikit-image
Free 1 RatingScikit-image is a collection algorithm for image processing. It is free to download and without restriction. We are proud of our high-quality code that has been peer-reviewed and is written by a large community of volunteers. Scikit-image is a Python library that provides a variety of image processing routines. This library is being developed by its community. Contributions are most welcome! Scikit-image is a reference library for scientific image analysis using Python. This is achieved by making it easy to use and easy to install. We take care when adding new dependencies. Sometimes we remove existing ones or make them optional. Our API has detailed docstrings that clarify the expected inputs and outputs for all functions. Conceptually identical arguments share the same name and position within a function signature. The library has close to 100% test coverage and all code is reviewed by at minimum two core developers before it is included. -
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SensePhoto
SenseTime
Deep learning technology provides multi-camera and one-camera portrait blur. Single-camera image quality enhancement is possible. Integration is easy with universal port interfaces. Customers receive professional and fast technical support. Integration is easy with universal port interfaces. Our industry-leading technology allows for a wide variety of product features. Expertise in AI and deep learning. He is also the leader of a big-data-driven image analysis algorithm. He is also a member of a professional product development team. Technology that is proprietary empowers businesses and services. SenseTime is a leader in AI software and is committed to creating an AI-empowered future by innovation. Aiming to advance the interconnection between the digital and physical worlds through AI. -
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Viesus
Viesus
$0.01/image Viesus is a platform designed for the automated enhancement of vast quantities of images, catering to industrial image processing for both print and digital platforms. With tools tailored for automatic refinement, restoration, and upscaling of pictures, Viesus aims to achieve optimal visual outcomes for every image. Crafted to industry standards, Viesus prioritizes handling large batches of images while ensuring speedy processing and delivering consistently high-quality results. Image Enhancement: Through Viesus Image Enhancement, images are fine-tuned naturally, considering each image's distinct characteristics. AI Upscaling: Viesus AI Upscaling elevates low-resolution images by amplifying their printable and pixel resolution, rendering them suitable for large-scale print jobs or premium advertising drives. Significantly, Viesus AI Upscaling was honored with the PRINTING United Pinnacle Product Award 2023 in the non-output division. -
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Microsoft Cognitive Toolkit
Microsoft
3 RatingsThe Microsoft Cognitive Toolkit is an open-source toolkit that allows commercial-grade distributed deep-learning. It describes neural networks using a directed graph, which is a series of computational steps. CNTK makes it easy to combine popular models such as feed-forward DNNs (CNNs), convolutional neural network (CNNs), and recurrent neural network (RNNs/LSTMs) with ease. CNTK implements stochastic grade descent (SGD, error-backpropagation) learning with automatic differentiation/parallelization across multiple GPUs or servers. CNTK can be used in your Python, C# or C++ programs or as a standalone machine learning tool via its own model description language (BrainScript). You can also use the CNTK model assessment functionality in your Java programs. CNTK is compatible with 64-bit Linux and 64-bit Windows operating system. You have two options to install CNTK: you can choose pre-compiled binary packages or you can compile the toolkit using the source available in GitHub. -
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JDeli
IDR Solutions
$1600 per yearJDeli can read and write images in HEIC format. It also provides a complete HEIC conversion between HEIC file formats and many other image file formats. JDeli can convert the image in one step. You can also read and write the HEIC file separately if you need to process the image using Java. Threading can improve the performance of your code reading and writing. JDeli can be used with threads, unlike ImageIO. JDeli has a similar API to ImageIO. It is also easy to switch between the two in your code. JDeli is actively being developed by IDRsolutions' development team, who use it in their other products. Unable to add a platform dependence? JDeli is 100% Java, and does not require plugins or native dependencies. -
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Fido
Fido
Fido is an open-source, lightweight, modular C++ machine-learning library. The library is geared towards embedded electronics and robotics. Fido contains implementations of reinforcement learning methods, genetic algorithms and trainable neural networks. It also includes a full-fledged robot simulator. Fido also includes a human-trainable robot controller system, as described by Truell and Gruenstein. Although the simulator is not available in the latest release, it can still be downloaded to experiment on the simulator branch. -
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Neural Magic
Neural Magic
The GPUs are fast at transferring data, but they have very limited locality of reference due to their small caches. They are designed to apply a lot compute to little data, and not a lot compute to a lot data. They are designed to run full layers of computation in order to fully fill their computational pipeline. (See Figure 1 below). Because large models have small memory sizes (tens to gigabytes), GPUs are placed together and models are distributed across them. This creates a complicated and painful software stack. It also requires synchronization and communication between multiple machines. The CPUs on the other side have much larger caches than GPUs and a lot of memory (terabytes). A typical CPU server may have memory equivalent to hundreds or even tens of GPUs. The CPU is ideal for a brain-like ML environment in which pieces of a large network are executed as needed. -
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Neuton AutoML
Neuton.AI
$0Neuton.AI, an automated solution, empowering users to build accurate predictive models and make smart predictions with: Zero code solution Zero need for technical skills Zero need for data science knowledge -
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Neural Designer is a data-science and machine learning platform that allows you to build, train, deploy, and maintain neural network models. This tool was created to allow innovative companies and research centres to focus on their applications, not on programming algorithms or programming techniques. Neural Designer does not require you to code or create block diagrams. Instead, the interface guides users through a series of clearly defined steps. Machine Learning can be applied in different industries. These are some examples of machine learning solutions: - In engineering: Performance optimization, quality improvement and fault detection - In banking, insurance: churn prevention and customer targeting. - In healthcare: medical diagnosis, prognosis and activity recognition, microarray analysis and drug design. Neural Designer's strength is its ability to intuitively build predictive models and perform complex operations.
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Darknet
Darknet
Darknet is an open-source framework for neural networks written in C and CUDA. It is easy to install and supports both CPU and GPU computation. The source code can be found on GitHub. You can also read more about Darknet's capabilities. Darknet is easy-to-install with only two dependencies: OpenCV if your preference is for a wider range of image types and CUDA if your preference is for GPU computation. Darknet is fast on the CPU, but it's about 500 times faster on the GPU. You will need an Nvidia GPU, and you'll need to install CUDA. Darknet defaults to using stb_image.h to load images. OpenCV is a better alternative to Darknet. It supports more formats, such as CMYK jpegs. Thanks to Obama! OpenCV allows you to view images, and detects without saving them to disk. You can classify images using popular models such as ResNet and ResNeXt. For NLP and time-series data, recurrent neural networks are a hot trend. -
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Chainer
Chainer
A powerful, flexible, intuitive framework for neural networks. Chainer supports CUDA computation. To leverage a GPU, it only takes a few lines. It can also be used on multiple GPUs without much effort. Chainer supports a variety of network architectures, including convnets, feed-forward nets, and recurrent nets. It also supports per batch architectures. Forward computation can include any control flow statement of Python without sacrificing the ability to backpropagate. It makes code easy to understand and debug. ChainerRLA is a library that implements several state-of-the art deep reinforcement algorithms. ChainerCVA is a collection that allows you to train and run neural network for computer vision tasks. Chainer supports CUDA computation. To leverage a GPU, it only takes a few lines. It can also be run on multiple GPUs without much effort. -
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GPT-4o
OpenAI
$5.00 /1M tokens GPT-4o (o for "omni") is an important step towards a more natural interaction between humans and computers. It accepts any combination as input, including text, audio and image, and can generate any combination of outputs, including text, audio and image. It can respond to audio in as little as 228 milliseconds with an average of 325 milliseconds. This is similar to the human response time in a conversation (opens in new window). It is as fast and cheaper than GPT-4 Turbo on text in English or code. However, it has a significant improvement in text in non-English language. GPT-4o performs better than existing models at audio and vision understanding. -
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Latent AI
Latent AI
We take the hard work out of AI processing on the edge. The Latent AI Efficient Inference Platform (LEIP) enables adaptive AI at edge by optimizing compute, energy, and memory without requiring modifications to existing AI/ML infrastructure or frameworks. LEIP is a fully-integrated modular workflow that can be used to build, quantify, and deploy edge AI neural network. Latent AI believes in a vibrant and sustainable future driven by the power of AI. Our mission is to enable the vast potential of AI that is efficient, practical and useful. We reduce the time to market with a Robust, Repeatable, and Reproducible workflow for edge AI. We help companies transform into an AI factory to make better products and services. -
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MXNet
The Apache Software Foundation
The hybrid front-end seamlessly switches between Gluon eager symbolic mode and Gluon imperative mode, providing flexibility and speed. The dual parameter server and Horovod support enable scaleable distributed training and performance optimization for research and production. Deep integration into Python, support for Scala and Julia, Clojure and Java, C++ and R. MXNet is supported by a wide range of tools and libraries that allow for use-cases in NLP, computer vision, time series, and other areas. Apache MXNet is an Apache Software Foundation (ASF) initiative currently incubating. It is sponsored by the Apache Incubator. All accepted projects must be incubated until further review determines that infrastructure, communications, decision-making, and decision-making processes have stabilized in a way consistent with other successful ASF projects. Join the MXNet scientific network to share, learn, and receive answers to your questions. -
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Zebra by Mipsology
Mipsology
Mipsology's Zebra is the ideal Deep Learning compute platform for neural network inference. Zebra seamlessly replaces or supplements CPUs/GPUs, allowing any type of neural network to compute more quickly, with lower power consumption and at a lower price. Zebra deploys quickly, seamlessly, without any knowledge of the underlying hardware technology, use specific compilation tools, or modifications to the neural network training, framework, or application. Zebra computes neural network at world-class speeds, setting a new standard in performance. Zebra can run on the highest throughput boards, all the way down to the smallest boards. The scaling allows for the required throughput in data centers, at edge or in the cloud. Zebra can accelerate any neural network, even user-defined ones. Zebra can process the same CPU/GPU-based neural network with the exact same accuracy and without any changes. -
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Imagga
Imagga
$79 per monthImagga's API allows you to create the next generation image recognition applications. Our machine learning technology allows you to create intelligent apps. Automatically assign tags for your images. A powerful API for image analysis and discovery. Your application can enable product discovery. A powerful API to build visual search capabilities. Your applications can now unlock facial recognition. A powerful API for building facial recognition. Our image A.I. can be trained To organize your photos in your own categories. Automatically categorize all your image content. A powerful API for instant image classification. Automated moderation of adult image content based on state-of-the-art image recognition technology. Beautiful thumbnails automatically generated. Powerful API for content-aware clipping. Let colors add meaning to your product photos. A powerful API for color extraction. -
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Libpixel
Libpixel
$ 15 Per monthThis is the only image processing solution that is simple and will save you hundreds of hours of engineering work. We can process your images as quickly as you need them. Only the originals are required. To request images that are the right width, height, or processed in another way, simply add the relevant parameters (URL) to the URL. A URL is used to stretch images to fill a 200x200 pixel box. We are aware that some entities may have special circumstances. Usually, this is due to regulatory restrictions. Therefore, we cannot rely on publicly-hosted image processing services. We only offer image processing and delivery. If you are looking for cloud storage or sharing files, we may not be the right choice. You must specify four parameters to crop an image: the origin x (which defines the top-left corner of the crop rectangle), and the dimensions (which determine the rectangle's size). -
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ImageGear
Accusoft
This document and image cleanup and processing toolkit allows developers the ability to quickly integrate document handling functions such as image manipulation, compression, manipulation, manipulation, manipulation, editing, manipulation, compression and image enhancement into their applications. ImageGear allows your application to clean up files such as deskew, line, and speckle removal, among others. ImageGear's color-processing tools can be used to improve image quality and reduce compressed file sizes. This SDK for image processing and document cleaning includes many APIs that allow image processing and clean-up. ImageGear can help you add functionality to your applications. Learn how ImageGear can meet all of your document lifecycle requirements. This PDF SDK allows.NET developers add robust PDF functionality to their applications. Users can view, annotate and compress pages. Discover all the PDF manipulation capabilities of ImageGear PDF and how it can enhance your application. -
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CloudSight API
CloudSight
Image recognition technology that gives you a complete understanding of your digital media. Our on-device computer vision system can provide a response time of less that 250ms. This is 4x faster than our API and doesn't require an internet connection. By simply scanning their phones around a room, users can identify objects in that space. This feature is exclusive to our on-device platform. Privacy concerns are almost eliminated by removing the requirement for data to be sent from the end-user device. Our API takes every precaution to protect your privacy. However, our on-device model raises security standards significantly. CloudSight will send you visual content. Our API will then generate a natural language description. Filter and categorize images. You can also monitor for inappropriate content and assign labels to all your digital media. -
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LEADTOOLS Imaging SDK
LEADTOOLS
LEADTOOLS ImagingSDK Technology provides the tools that developers need to add powerful imaging technology into their applications. LEADTOOLS Imaging features are based on more 32 years of experience in imaging development. They include image compression, image viewing, more then 200 image processing functions and image viewers. Common dialogs, TWAIN and WIA scanning, screen capture and printing. LEADTOOLS allows developers to create applications that can load, save, convert, and convert many proprietary and industry-standard formats. LEAD Technologies is committed in maintaining and expanding the most extensive support for file formats on the marketplace. It currently supports more than 150 raster and vector file formats as well as sub-formats. -
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imgix
Zebrafish Labs
FreeSimple API, imgix transforms and optimizes images for websites and apps that use simple URL parameters. We don't charge for creating variations of Master Images. The service is open to all creative ideas. There are over 100 image operations that can be done in real time. You also have client libraries and CMS plugins to make it easy to integrate with your product. With a global CDN optimized for visual content, you can quickly deliver optimized images to any device. Search, sort, and organize all your cloud storage images. Simple URL parameters allow you to resize, crop, or enhance your images. Intelligent, automated compression that removes unnecessary bytes Customers can see images quickly thanks to imgix’s global CDN and caching. Imgix Image Management. Transform your cloud bucket to a sophisticated platform that allows for you to see the potential of your images. -
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Cloudmersive
Cloudmersive
3 RatingsVirus API allows you to scan files and identify security problems with content. A Virus Scanning and Reverse Proxy Server protects your web application and web APIs from viruses uploaded automatically. Protect any object or file in Google Cloud Platform (GCP), Cloud Storage automatically from viruses and malware. Protect any SharePoint Document Libraries and Sites from malware and viruses automatically, without code changes, in real-time. Our advanced Deep Learning OCR APIs allow you to convert scanned documents and photos into rich text. Automatically unrotates and unskews the images when necessary. The validation APIs allow you to validate data. Verify that an E-mail address you receive is legitimate. Verify that the domain is legitimate. You can check the IP address and where it is located. The validation API provides all this information and more. -
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Blitline
Blitline
$9 per monthSpend less & scale your apps with ease with Blitline's Image Processing-as-a-Service (IPaaS). Blitline offers the most affordable Image Processing as a Service solution (IPaaS), for media and software companies who need bulk image processing and media processing at scale. The Blitline JSON API offers a better alternative than Open Source solutions that can bottleneck user experience innovation and costly outsourced services that are primarily focused on image and video formats. The Blitline is an all-in-one enterprise solution which will increase your secure media processing performance while lowering your total cost of ownership. Massive. We have a large number of machines. Always on demand. Smart. Smart. -
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Cogniac
Cogniac
Cogniac's no code solution allows organizations to take advantage of the latest developments in Artificial Intelligence and convolutional neural network technology to deliver extraordinary operational performance. Cogniac's AI platform for machine vision enables enterprises to reach Industry 4.0 standards via visual data management and automated automation. Cogniac helps organizations' operations divisions deliver smart continuous improvement. Cogniac's user interface was designed to be used by non-technical users. The Cogniac platform's drag-and-drop nature allows subject matter experts and other specialists to concentrate on the tasks that are most important. Cogniac can detect defects in as few as 100 images. After being trained with 25 approved images and 75 deficient images, Cogniac AI can deliver results comparable to human subject matter experts within hours. -
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Keras is an API that is designed for humans, not machines. Keras follows best practices to reduce cognitive load. It offers consistent and simple APIs, minimizes the number required for common use cases, provides clear and actionable error messages, as well as providing clear and actionable error messages. It also includes extensive documentation and developer guides. Keras is the most popular deep learning framework among top-5 Kaggle winning teams. Keras makes it easy to run experiments and allows you to test more ideas than your competitors, faster. This is how you win. Keras, built on top of TensorFlow2.0, is an industry-strength platform that can scale to large clusters (or entire TPU pods) of GPUs. It's possible and easy. TensorFlow's full deployment capabilities are available to you. Keras models can be exported to JavaScript to run in the browser or to TF Lite for embedded devices on iOS, Android and embedded devices. Keras models can also be served via a web API.
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YandexART
Yandex
YandexART, a diffusion neural net by Yandex, is designed for image and videos creation. This new neural model is a global leader in image generation quality among generative models. It is integrated into Yandex's services, such as Yandex Business or Shedevrum. It generates images and video using the cascade diffusion technique. This updated version of the neural network is already operational in the Shedevrum app, improving user experiences. YandexART, the engine behind Shedevrum, boasts a massive scale with 5 billion parameters. It was trained on a dataset of 330,000,000 images and their corresponding text descriptions. Shedevrum consistently produces high-quality content through the combination of a refined dataset with a proprietary text encoding algorithm and reinforcement learning. -
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Deci
Deci AI
Deci's deep learning platform powered by Neural architecture Search allows you to quickly build, optimize, deploy, and deploy accurate models. You can instantly achieve accuracy and runtime performance that is superior to SoTA models in any use case or inference hardware. Automated tools make it easier to reach production. No more endless iterations or dozens of libraries. Allow new use cases for resource-constrained devices and cut down on your cloud computing costs by up to 80% Deci's NAS-based AutoNAC engine automatically finds the most appropriate architectures for your application, hardware, and performance goals. Automately compile and quantify your models using the best of breed compilers. Also, quickly evaluate different production settings. -
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GPT-3.5 is the next evolution to GPT 3 large language model, OpenAI. GPT-3.5 models are able to understand and generate natural languages. There are four main models available with different power levels that can be used for different tasks. The main GPT-3.5 models can be used with the text completion endpoint. There are models that can be used with other endpoints. Davinci is the most versatile model family. It can perform all tasks that other models can do, often with less instruction. Davinci is the best choice for applications that require a deep understanding of the content. This includes summarizations for specific audiences and creative content generation. These higher capabilities mean that Davinci is more expensive per API call and takes longer to process than other models.
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GPT-3 models are capable of understanding and generating natural language. There are four main models available, each with a different level of power and suitable for different tasks. Ada is the fastest and most capable model while Davinci is our most powerful. GPT-3 models are designed to be used in conjunction with the text completion endpoint. There are models that can be used with other endpoints. Davinci is the most versatile model family. It can perform all tasks that other models can do, often with less instruction. Davinci is the best choice for applications that require a deep understanding of the content. This includes summarizations for specific audiences and creative content generation. These higher capabilities mean that Davinci is more expensive per API call and takes longer to process than other models.
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Whisper
OpenAI
We have developed and are open-sourcing Whisper, a neural network that approximates human-level robustness in English speech recognition. Whisper is an automated speech recognition (ASR), system that was trained using 680,000 hours of multilingual, multitask supervised data from the internet. The use of such a diverse dataset results in a better resistance to accents, background noise, technical language, and other linguistic issues. It also allows transcription in multiple languages and translation from these languages into English. We provide inference code and open-sourcing models to help you build useful applications and further research on robust speech processing. The Whisper architecture is an end-to-end, simple approach that can be used as an encoder/decoder Transformer. The input audio is divided into 30-second chunks and converted into a log Mel spectrogram. This then goes into an encoder. -
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NVIDIA GPU-Optimized AMI
Amazon
$3.06 per hourThe NVIDIA GPU Optimized AMI is a virtual image that accelerates your GPU-accelerated Machine Learning and Deep Learning workloads. This AMI allows you to spin up a GPU accelerated EC2 VM in minutes, with a preinstalled Ubuntu OS and GPU driver. Docker, NVIDIA container toolkit, and Docker are also included. This AMI provides access to NVIDIA’s NGC Catalog. It is a hub of GPU-optimized software for pulling and running performance-tuned docker containers that have been tested and certified by NVIDIA. The NGC Catalog provides free access to containerized AI and HPC applications. It also includes pre-trained AI models, AI SDKs, and other resources. This GPU-optimized AMI comes free, but you can purchase enterprise support through NVIDIA Enterprise. Scroll down to the 'Support information' section to find out how to get support for AMI. -
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OpenCV
OpenCV
FreeOpenCV (Open Source Computer Vision Library), is an open-source machine learning and computer vision software library. OpenCV was created to provide a common infrastructure to support computer vision applications and accelerate machine perception in commercial products. OpenCV is a BSD-licensed product that makes it easy to modify and use the code by businesses. The library contains more than 2500 optimized algorithms. This includes a comprehensive set both of classic and modern computer vision and machine-learning algorithms. These algorithms can be used for recognizing faces, identifying objects, tracking camera movements, classifying human actions in videos and producing 3D point clouds from stereo-cameras. They can also be used to stitch images together to create a high resolution image of the entire scene, find similar images from a database, remove red eyes from images taken with flash, recognize scenery, and follow eye movements. -
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Apache Mahout
Apache Software Foundation
Apache Mahout is an incredibly powerful, scalable and versatile machine-learning library that was designed for distributed data processing. It provides a set of algorithms that can be used for a variety of tasks, such as classification, clustering and recommendation. Mahout is built on top of Apache Hadoop and uses MapReduce and Spark for data processing. Apache Mahout(TM), a distributed linear-algebra framework, is a mathematically expressive Scala DSL that allows mathematicians to quickly implement their algorithms. Apache Spark is recommended as the default distributed back-end, but can be extended to work with other distributed backends. Matrix computations play a key role in many scientific and engineering applications such as machine learning, data analysis, and computer vision. Apache Mahout is designed for large-scale data processing, leveraging Hadoop and Spark. -
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ImageJ
ImageJ
You can create rectangular, irregular, or elliptical area selections. You can create line and point selections. Edit selections and create them automatically using the wand. Fill, clear, filter, measure, or draw selections. You can save your selections and transfer them into other images. Smoothing, sharpening and edge detection are all possible on both 8-bit grayscale images and RGB color images. Adjust brightness and contrast for 8-, 16-, and 32 bit images interactively Measure area, average, standard deviation, min. and maximum of selections or the entire image. Measure lengths and angles. Use real world measurement units like millimeters. Calibrate using density standards. Generate profile plots and histograms.