Search Results for "framework-arduinoststm32" - Page 27
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Showing 1339 open source projects for "framework-arduinoststm32"
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King Phisher
Phishing Campaign Toolkit
King Phisher is an open source tool that can simulate real world phishing attacks. It has an easy-to-use, flexible architecture that allows for full control over both emails and server content. It is useful for running awareness campaigns and training, and can only be used for legal applications when the explicit permission of the targeted organization has been obtained. -
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hug
Embrace the APIs of the future. For developing APIs
hug aims to make developing Python-driven APIs as simple as possible, but no simpler. As a result, it drastically simplifies Python API development. Make developing a Python-driven API as succinct as a written definition. The framework should encourage code that self-documents. It should be fast. A developer should never feel the need to look somewhere else for performance reasons. Writing tests for APIs written on-top of hug should be easy and intuitive. Magic done once, in an API framework, is better than pushing the problem set to the user of the API framework. ... -
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Neural MMO
Code for the paper "Neural MMO: A Massively Multiagent Game..."
...The project is built to test scalability in multi-agent reinforcement learning, with features such as procedurally generated terrain and configurable game mechanics. While the original release has since been succeeded by newer versions maintained outside OpenAI, it remains a landmark framework for studying large-scale agent interactions in complex environments. -
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An open source framework for LC-MS based proteomics and metabolomics. OpenMS offers data structures and algorithms for the processing of mass spectrometry data. The library is written in C++. Our source code and wiki lives on GitHub (https://github.com/OpenMS/OpenMS).
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MMF
A modular framework for vision & language multimodal research
MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. MMF is designed from ground up to let you focus on what matters, your model, by providing boilerplate code for distributed training, common datasets and state-of-the-art pre-trained baselines out-of-the-box. -
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WALKOFF
A flexible, easy to use, automation framework
Faster, smarter, cheaper operations through automation. WALKOFF puts the tools in your hands to easily automate the tedious repetitive tasks dragging your operations down. Act smarter with WALKOFF by automatically gathering data, analyzing data, or visualizing data customized to your requirements. Act faster with WALKOFF by integrating the capabilities you already own to dynamically respond on your terms to your fast-moving environment. Drag and drop workflow editor. Sharable apps and... -
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CCZero (中国象棋Zero)
Implement AlphaZero/AlphaGo Zero methods on Chinese chess
ChineseChess-AlphaZero is a project that implements the AlphaZero algorithm for the game of Chinese Chess (Xiangqi). It adapts DeepMind’s AlphaZero method—combining neural networks and Monte Carlo Tree Search (MCTS)—to learn and play Chinese Chess without prior human data. The system includes self-play, training, and evaluation pipelines tailored to Xiangqi's unique game mechanics. -
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Olex2 is visualisation software for small-molecule crystallography developed at Durham University/EPSRC. It provides comprehensive tools for crystallographic model manipulation for the end user and an extensible development framework for programmers. The project has been supported by Olexsys Ltd since 2010.
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Coach
Enables easy experimentation with state of the art algorithms
Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. -
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A symbolic framework for C++, Python and Octave implementing automatic differentiation by source code transformation in forward and reverse modes on sparse matrix-valued computational graphs.
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maskrcnn-benchmark
Fast, modular reference implementation of Instance Segmentation
Mask R-CNN Benchmark is a PyTorch-based framework that provides high-performance implementations of object detection, instance segmentation, and keypoint detection models. Originally built to benchmark Mask R-CNN and related models, it offers a clean, modular design to train and evaluate detection systems efficiently on standard datasets like COCO. The framework integrates critical components—region proposal networks (RPNs), RoIAlign layers, mask heads, and backbone architectures such as ResNet and FPN—optimized for both accuracy and speed. ... -
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TestLink-API-Python-client
Python XML-RPC client for TestLink
TestLink-API-Python-client is a Python XML-RPC client for TestLink Initially based on James Stock testlink-api-python-client R7 and Olivier Renault JinFeng idea - an interaction of TestLink, Robot Framework and Jenkins. TestLink-API-Python-client delivers two main classes - TestlinkAPIGeneric - Implements the Testlink API methods as generic PY methods with error handling - TestlinkAPIClient - Inherits from TestlinkAPIGeneric and defines service methods like "copyTCnewVersion" and the helper class - TestLinkHelper - search connection parameter from environment variables and command line arguments -
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RefineNet
RefineNet: Multi-Path Refinement Networks
RefineNet is a MATLAB-based framework for semantic image segmentation and general dense prediction tasks. It implements the architecture presented in the CVPR 2017 paper RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation and its extended version published in TPAMI 2019. The framework uses multi-path refinement and improved residual pooling to achieve high-quality segmentation results across multiple benchmark datasets. -
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Active Learning
Framework and examples for active learning with machine learning model
...It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks. -
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Offensive Web Testing Framework
Offensive Web Testing Framework (OWTF), is a framework
OWASP OWTF is a project focused on penetration testing efficiency and alignment of security tests to security standards like the OWASP Testing Guide (v3 and v4), the OWASP Top 10, PTES and NIST so that pentesters will have more time to see the big picture and think out of the box. More efficiently find, verify and combine vulnerabilities. Have time to investigate complex vulnerabilities like business logic/architectural flaws or virtual hosting sessions. Perform more tactical/targeted... -
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MUSE
A library for Multilingual Unsupervised or Supervised word Embeddings
MUSE is a framework for learning multilingual word embeddings that live in a shared space, enabling bilingual lexicon induction, cross-lingual retrieval, and zero-shot transfer. It supports both supervised alignment with seed dictionaries and unsupervised alignment that starts without parallel data by using adversarial initialization followed by Procrustes refinement. -
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X-JHBuild is a framework for working with multiple repositories. It makes use of JHBuild and is specialized for working with modules that use Git as VCS.
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Rasa Core
Rasa Core is now part of the Rasa repo
Rasa is an open source machine learning framework to automate text and voice-based conversations. With Rasa, you can build contextual assistants. Rasa helps you build contextual assistants capable of having layered conversations with lots of back-and-forth. In order for a human to have a meaningful exchange with a contextual assistant, the assistant needs to be able to use context to build on things that were previously discussed – Rasa enables you to build assistants that can do this in a scalable way. -
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Django REST Pandas
Serves up Pandas dataframes via the Django REST Framework
Django REST Pandas (DRP) provides a simple way to generate and serve pandas DataFrames via the Django REST Framework. The resulting API can serve up CSV (and a number of other formats for consumption by a client-side visualization tool like d3.js. The design philosophy of DRP enforces a strict separation between data and presentation. This keeps the implementation simple, but also has the nice side effect of making it trivial to provide the source data for your visualizations. ... -
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automl-gs
Provide an input CSV and a target field to predict, generate a model
...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 definition interface to getting an optimized model and data transformation pipeline in multiple popular ML/DL frameworks, with minimal Python dependencies (pandas + scikit-learn + your framework of choice). automl-gs is designed for citizen data scientists and engineers without a deep statistical background under the philosophy that you don't need to know any modern data preprocessing and machine learning engineering techniques to create a powerful prediction workflow. -
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Invenio
Invenio digital library framework
Invenio is a highly customizable open-source framework for building large-scale digital repositories and research data platforms. Developed by CERN, it is designed to manage, index, and provide access to metadata-rich content such as publications, datasets, and multimedia files. Invenio provides a modular architecture, making it suitable for libraries, archives, and research institutions. -
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Scalable Distributed Deep-RL
A TensorFlow implementation of Scalable Distributed Deep-RL
Scalable Agent is the open implementation of IMPALA (Importance Weighted Actor-Learner Architectures), a highly scalable distributed reinforcement learning framework developed by Google DeepMind. IMPALA introduced a new paradigm for efficiently training agents across large-scale environments by decoupling acting and learning processes. In this architecture, multiple actor processes interact with their environments in parallel to collect trajectories, which are then asynchronously sent to a centralized learner for policy updates. ... -
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ShadowSocksShare
Python ShadowSocks framework
This project obtains the shared ss(r) account from the ss(r) shared website crawler, redistributes the account and generates a subscription link by parsing and verifying the account connectivity. Since Google plus will be closed on April 2, 2019, almost all the available accounts crawled before come from Google plus. So if you are building your own website, please keep an eye on the updates of this project and redeploy using the latest source code. -
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PythonQt
Dynamic Python binding for Qt Applications
NOTE: PythonQt has been moved to https://github.com/MeVisLab/pythonqt PythonQt is a dynamic and lightweight script binding of the Qt framework to the Python language. It can be easily embedded into Qt applications and makes any QObject derived object scriptable via Python without the need of wrapper code generation. -
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django-dynamic-scraper
Creating Scrapy scrapers via the Django admin interface
Django Dynamic Scraper (DDS) is an app for Django build on top of the scraping framework Scrapy. While preserving many of the features of Scrapy it lets you dynamically create and manage spiders via the Django admin interface. With Django Dynamic Scraper (DDS) you can define your Scrapy scrapers dynamically via the Django admin interface and save your scraped items in the database you defined for your Django project.
