Search Results for "linux os super"
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Showing 32 open source projects for "linux os super"
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1
Final2x
2^x Image Super-Resolution
...The tool is available in English, Chinese, and Japanese, allowing users from different countries to enjoy the benefits of super-resolution. The tool is available for Windows x64/arm64, MacOS x64/arm64, and Linux x64, allowing users to enjoy the benefits of super-resolution regardless of their operating system. -
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dlib
Toolkit for making machine learning and data analysis applications
...Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is about 1 to 4. The library is tested regularly on MS Windows, Linux, and Mac OS X systems. No other packages are required to use the library, only APIs that are provided by an out of the box OS are needed. There is no installation or configure step needed before you can use the library. All operating system specific code is isolated inside the OS abstraction layers which are kept as small as possible. -
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emgucv
Cross platform .Net wrapper to the OpenCV image processing library
Emgu CV is a cross platform .Net wrapper to the OpenCV image processing library. Allowing OpenCV functions to be called from .NET compatible languages. The wrapper can be compiled by Visual Studio and Unity, it can run on Windows, Linux, Mac OS, iOS and Android. -
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ML.NET
Open source and cross-platform machine learning framework for .NET
With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem. ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps. ML.NET offers Model Builder (a simple UI tool) and ML.NET CLI to make it super easy to build custom ML Models. These tools use Automated ML (AutoML), a cutting edge technology that... -
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5
PyBroker
Algorithmic Trading in Python with Machine Learning
Are you looking to enhance your trading strategies with the power of Python and machine learning? Then you need to check out PyBroker! This Python framework is designed for developing algorithmic trading strategies, with a focus on strategies that use machine learning. With PyBroker, you can easily create and fine-tune trading rules, build powerful models, and gain valuable insights into your strategy’s performance. -
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Evidently
Evaluate and monitor ML models from validation to production
Evidently is an open-source Python library for data scientists and ML engineers. It helps evaluate, test, and monitor ML models from validation to production. It works with tabular, text data and embeddings. -
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satellite-image-deep-learning
Resources for deep learning with satellite & aerial imagery
This page lists resources for performing deep learning on satellite imagery. To a lesser extent classical Machine learning (e.g. random forests) are also discussed, as are classical image processing techniques. Note there is a huge volume of academic literature published on these topics, and this repository does not seek to index them all but rather list approachable resources with published code that will benefit both the research and developer communities. If you find this work useful... -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of... -
9
OnnxStream
Lightweight inference library for ONNX files, written in C++
The challenge is to run Stable Diffusion 1.5, which includes a large transformer model with almost 1 billion parameters, on a Raspberry Pi Zero 2, which is a microcomputer with 512MB of RAM, without adding more swap space and without offloading intermediate results on disk. The recommended minimum RAM/VRAM for Stable Diffusion 1.5 is typically 8GB. Generally, major machine learning frameworks and libraries are focused on minimizing inference latency and/or maximizing throughput, all of which... -
Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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Adaptive Intelligence
Adaptive Intelligence also known as "Artificial General Intelligence"
Adaptive Intelligence is the implementation of neural science, forensic psychology , behavioral science with machine-learning and artificial intelligence to provide advanced automated software platforms with the ability to adjust and thrive in dynamic environments by combining cognitive flexibility, emotional regulation, resilience, and practical problem-solving skills. -
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PumpkinBook
Machine Learning formula derivation and analysis
All the contents of the Pumpkin Book are expressed with the content of the Mr. Zhou Zhihua's "Machine Learning" Watermelon Book as the pre-knowledge, so the best way to use the Pumpkin Book is to use the Watermelon Book as the main line. Please refer to it when you encounter a formula that you cannot derive or cannot understand. We strive to explain and derive each formula from the perspective of undergraduate mathematics. Therefore, we usually give out the mathematics knowledge of the super... -
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sense2vec
Contextually-keyed word vectors
sense2vec (Trask et. al, 2015) is a nice twist on word2vec that lets you learn more interesting and detailed word vectors. This library is a simple Python implementation for loading, querying and training sense2vec models. For more details, check out our blog post. To explore the semantic similarities across all Reddit comments of 2015 and 2019, see the interactive demo. -
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Unitag is a language-independent Unicode-based part-of-speech tagging system. Written entirely in ANSI-compatible C, it should (in theory) compile on any OS, but has been tested on 32-bit Windows.
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14
KAIR
Image Restoration Toolbox (PyTorch). Training and testing codes
Image restoration toolbox (PyTorch). Training and testing codes for DPIR, USRNet, DnCNN, FFDNet, SRMD, DPSR, BSR/GAN, SwinIR. -
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Tez
Tez is a super-simple and lightweight Trainer for PyTorch
Tez is a super-simple and lightweight Trainer for PyTorch. It also comes with many utils that you can use to tackle over 90% of deep learning projects in PyTorch. tez (तेज़ / تیز) means sharp, fast & active. This is a simple, to-the-point, library to make your PyTorch training easy. This library is in early-stage currently! So, there might be breaking changes. Currently, tez supports cpu, single gpu and multi-gpu & tpu training. More coming soon! Using tez is super-easy. We don't want you to... -
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DeepMosaics
Automatically remove the mosaics in images and videos, or add mosaics
Automatically remove the mosaics in images and videos, or add mosaics to them. This project is based on "semantic segmentation" and "Image-to-Image Translation". You can either run DeepMosaics via a pre-built binary package, or from source. Run time depends on the computer's performance (GPU version has better performance but requires CUDA to be installed). Different pre-trained models are suitable for different effects.[Introduction to pre-trained models]. -
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Pwnagotchi
Deep Reinforcement learning instrumenting bettercap for WiFi pwning
Pwnagotchi is an A2C-based “AI” powered by bettercap and running on a Raspberry Pi Zero W that learns from its surrounding WiFi environment in order to maximize the crackable WPA key material it captures (either through passive sniffing or by performing deauthentication and association attacks). This material is collected on disk as PCAP files containing any form of handshake supported by hashcat, including full and half WPA handshakes as well as PMKIDs. Instead of merely playing Super Mario... -
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Gluon CV Toolkit
Gluon CV Toolkit
GluonCV provides implementations of state-of-the-art (SOTA) deep learning algorithms in computer vision. It aims to help engineers, researchers, and students quickly prototype products, validate new ideas and learn computer vision. It features training scripts that reproduce SOTA results reported in latest papers, a large set of pre-trained models, carefully designed APIs and easy-to-understand implementations and community support. From fundamental image classification, object detection,... -
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GIMP ML
AI for GNU Image Manipulation Program
This repository introduces GIMP3-ML, a set of Python plugins for the widely popular GNU Image Manipulation Program (GIMP). It enables the use of recent advances in computer vision to the conventional image editing pipeline. Applications from deep learning such as monocular depth estimation, semantic segmentation, mask generative adversarial networks, image super-resolution, de-noising and coloring have been incorporated with GIMP through Python-based plugins. Additionally, operations on... -
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TensorFlow Object Counting API
The TensorFlow Object Counting API is an open source framework
The TensorFlow Object Counting API is an open source framework built on top of TensorFlow and Keras that makes it easy to develop object counting systems. Please contact if you need professional object detection & tracking & counting project with super high accuracy and reliability! You can train TensorFlow models with your own training data to built your own custom object counter system! If you want to learn how to do it, please check one of the sample projects, which cover some of the... -
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Image Super-Resolution (ISR)
Super-scale your images and run experiments with Residual Dense
The goal of this project is to upscale and improve the quality of low-resolution images. This project contains Keras implementations of different Residual Dense Networks for Single Image Super-Resolution (ISR) as well as scripts to train these networks using content and adversarial loss components. Docker scripts and Google Colab notebooks are available to carry training and prediction. Also, we provide scripts to facilitate training on the cloud with AWS and Nvidia-docker with only a few... -
22
nGraph
nGraph has moved to OpenVINO
Frameworks using nGraph Compiler stack to execute workloads have shown up to 45X performance boost when compared to native framework implementations. We've also seen performance boosts running workloads that are not included on the list of Validated workloads, thanks to nGraph's powerful subgraph pattern matching. Additionally, we have integrated nGraph with PlaidML to provide deep learning performance acceleration on Intel, nVidia, & AMD GPUs. nGraph Compiler aims to accelerate developing... -
23
NeuralCoref
Fast Coreference Resolution in spaCy with Neural Networks
NeuralCoref is a pipeline extension for spaCy 2.1+ which annotates and resolves coreference clusters using a neural network. NeuralCoref is production-ready, integrated in spaCy's NLP pipeline and extensible to new training datasets. For a brief introduction to coreference resolution and NeuralCoref, please refer to our blog post. NeuralCoref is written in Python/Cython and comes with a pre-trained statistical model for English only. NeuralCoref is accompanied by a visualization client... -
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automl-gs
Provide an input CSV and a target field to predict, generate a model
Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learning model plus native Python code pipelines allowing you to integrate that model into any prediction workflow. No black box: you can see exactly how the data is processed, and how the model is constructed, and you can make tweaks as necessary. automl-gs is an AutoML tool which, unlike Microsoft's NNI, Uber's Ludwig, and TPOT, offers a zero code/model... -
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NeuroNER
Named-entity recognition using neural networks
Named-entity recognition (NER) aims at identifying entities of interest in the text, such as location, organization and temporal expression. Identified entities can be used in various downstream applications such as patient note de-identification and information extraction systems. They can also be used as features for machine learning systems for other natural language processing tasks. Leverages the state-of-the-art prediction capabilities of neural networks (a.k.a. "deep learning") Is...
