Coding & Development

This open-source project provides a Tensorflow 2.x based implementation of state-of-the-art models for single image super-resolution, including Enhanced Deep Residual Networks (EDSR), Wide Activation for Efficient and Accurate Image Super-Resolution (WDSR), and Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (SRGAN). It offers a high-level training API, enabling users to train models as described in the respective papers and fine-tune EDSR and WDSR models within an SRGAN context. The tool includes a DIV2K data provider for automatic dataset downloads and offers pre-trained weights for quick setup. It's ideal for developers and researchers working on image processing and computer vision tasks.

Deep-Reinforcement-Learning-Hands-On-Second-Edition is an open-source educational resource published by Packt, designed to help users learn and apply deep reinforcement learning techniques. The GitHub repository provides comprehensive code examples and materials, making it a practical companion for the associated book. It is actively maintained to ensure dependency versions are kept up-to-date, with specific code branches available for major PyTorch versions (e.g., 1.3 and 1.7) to accommodate compatibility needs. The resource includes detailed instructions for setting up a virtual environment using Anaconda, installing PyTorch, and managing other dependencies, making it accessible for hands-on experimentation and learning.

SphereFace offers a comprehensive open-source implementation of the SphereFace algorithm, a deep hypersphere embedding method for face recognition. This tool provides a full pipeline covering face detection, alignment, and recognition, making it valuable for researchers and developers in computer vision. It includes detailed instructions for installation and usage, demonstrating how to train models on datasets like CASIA-WebFace and evaluate performance on LFW. The repository also features various network architectures, including SphereFace-20, and highlights its state-of-the-art verification performance in challenges like MegaFace. Additionally, it provides insights into the underlying mathematical concepts and practical considerations for training, such as gradient normalization and convergence difficulties, along with links to third-party re-implementations and related angular margin learning resources.

SSL4MIS (Semi Supervised Learning for Medical Image Segmentation) is a comprehensive resource for researchers and developers focusing on medical image analysis. It offers a curated collection of literature reviews and practical code implementations for semi-supervised learning techniques. The repository includes re-implementations of various semi-supervised methods such as Mean Teacher, Entropy Minimization, and FixMatch, adapted for medical image segmentation. Additionally, it supports a range of 2D and 3D backbone networks like UNet, nnUNet, and Swin-UNet. This project aims to establish a benchmark for semi-supervised medical image segmentation, fostering easier evaluation and fair comparison within the medical image computing community. It also covers active learning and source-free domain adaptation for medical image analysis.

dque is a fast, embedded, durable queue specifically designed for Go applications. It offers a persistent and scalable FIFO (First In, First Out) queuing solution that is compiled directly into your Golang program. Key features include durability, ensuring data survives program restarts, and scalability, as it's limited by disk space rather than RAM. dque supports concurrent usage and provides two performance modes: 'safe' for maximum data integrity with fsync on every operation, and 'turbo' for faster operations by letting the OS batch changes, with the option to manually flush. The queue is implemented using configurable segments, with only the head and tail segments held in memory, making it efficient for large queues. It's an ideal tool for developers needing a reliable, embedded message queuing system within their Go projects.

Mouse Hackathon is a dynamic platform designed for creative innovation using AI, specifically structured around 1-minute challenges. It serves as a Hugging Face Space by VIDraft, offering a collaborative environment for AI enthusiasts and innovators. The platform allows users to participate in the MOUSE-I Hackathon, providing clear information on dates, prize amounts, and participation steps. It also features language switching between English and Korean, alongside a news view, to keep participants informed and engaged. This tool is ideal for those looking to quickly experiment with AI concepts and engage in rapid prototyping within a competitive yet supportive hackathon setting.

synthetic-computer-vision is a GitHub repository dedicated to tracking and organizing resources related to the use of synthetic images in computer vision research. It serves as a valuable hub for researchers, offering a curated list of synthetic datasets such as SunCG, Minos, and Synthia, alongside various tools like AirSim, CARLA, and UnrealCV. The repository also includes a collection of relevant academic publications, categorized by year, with links to papers, code, and project pages. Users are encouraged to contribute by adding missing works or updating existing information through pull requests, making it a collaborative and up-to-date resource for the computer vision community.

tensorflow-yolo offers a TensorFlow-based implementation of the YOLO (You Only Look Once) real-time object detection system. This open-source project allows developers and researchers to train and test their own object detection models using TensorFlow 1.0. The repository includes instructions for downloading pre-trained models, setting up training data using Pascal-VOC2007, and converting custom data to the required text_record format. It provides the necessary tools and scripts for preprocessing data, configuring training parameters, and running demonstrations, making it a valuable resource for those working with real-time object detection.

DeepRL-TensorFlow2 is a GitHub repository offering straightforward implementations of a wide array of Deep Reinforcement Learning (DRL) algorithms, all built with TensorFlow2. The project prioritizes code clarity, making it an excellent resource for students and researchers delving into DRL. Each algorithm is contained within a single Python script, simplifying the learning process by eliminating the need to navigate multiple files. The repository is actively maintained and continuously updated with new DRL algorithms. It currently includes implementations for DQN, DRQN, DoubleDQN, DuelingDQN, A2C, A3C, PPO, and DDPG, with TRPO, TD3, and SAC noted as planned additions. The project also provides code snippets illustrating the core ideas behind each algorithm, such as using target networks and replay buffers in DQN, or advantage functions in A2C.

The demo-self-driving project is an interactive Streamlit application designed to showcase the Udacity self-driving-car dataset. It integrates real-time object detection capabilities using the YOLO (You Only Look Once) algorithm, providing a practical example of computer vision in action. The entire application is implemented in less than 300 lines of Python code, highlighting Streamlit's efficiency for building interactive data applications. This tool serves as an excellent resource for developers and data scientists interested in exploring self-driving car datasets and real-time object detection with a user-friendly interface.

defmt, short for "deferred formatting," is a highly efficient logging framework specifically designed for resource-constrained embedded systems, such as microcontrollers. It minimizes resource usage during the logging process by deferring formatting operations. The framework includes on-target code for efficient logging, along with procedural macros for easy integration. It also provides CLI utilities and host libraries for decoding and parsing defmt-encoded logs, enabling developers to analyze log data on a host machine. defmt supports various on-target log transport mechanisms, including RTT, ITM, and semihosting, and integrates with panic-probe for panic! handling. It is part of the Knurling project by Ferrous Systems, aimed at improving embedded systems development tooling.

tmrl is a comprehensive open-source Python framework for training Deep Reinforcement Learning (RL) AIs in real-time applications, such as robotics, video games, and high-frequency control. It features a distributed architecture, enabling secure remote training and fine-grained customizability. The framework comes with a readily implemented example pipeline for the TrackMania 2020 racing video game, allowing users to train policies with state-of-the-art algorithms like Soft Actor-Critic (SAC) and Randomized Ensembled Double Q-Learning (REDQ). tmrl also provides a Gymnasium environment for TrackMania, making it easy to integrate into existing training frameworks. It supports both vision-based (CNN for raw images) and simpler rangefinder (MLP for LIDAR) observations, and offers analog control via a virtual gamepad.

WebWorldWind is an Open Source JavaScript SDK developed by NASA, with contributions from the European Space Agency, designed for creating geo-browser web applications. It allows developers to embed a 3D globe directly into HTML5 web pages, providing a geographic context with terrain and various shapes for displaying and interacting with geo-located information in both 3D and 2D. The SDK automatically retrieves high-resolution terrain and imagery from remote servers as needed, while also supporting custom terrain, imagery, 3D shapes, and position markings. Key features include improvements to COLLADA 3D model support, the ability to obtain click locations in 3D models, and enhanced Well-Known Text format support. It is licensed under the Apache License, Version 2.0.

YOLOv13 is an open-source implementation for real-time object detection, leveraging hypergraph-enhanced adaptive visual perception. It introduces HyperACE for exploring high-order correlations between pixels in multi-scale feature maps and FullPAD for fine-grained information flow and representational synergy across the entire detection pipeline. The tool also incorporates model lightweighting via DS-based Blocks, replacing large-kernel convolutions with depthwise separable convolutions for faster inference without sacrificing accuracy. YOLOv13 is available in Nano, Small, Large, and X-Large variants, offering cutting-edge performance and efficiency for various object detection tasks. It supports deployment on platforms like Huawei Ascend and Rockchip, and includes a FastAPI REST API.

Maml is an open-source code repository for Model-Agnostic Meta-Learning (MAML), a technique designed for the fast adaptation of deep networks. Developed by cbfinn, this repository provides the foundational code accompanying the paper "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks" (Finn et al., ICML 2017). It specifically includes implementations for few-shot supervised learning domain experiments, covering tasks such as sinusoid regression, Omniglot classification, and MiniImagenet classification. The project is built using Python 2.* or 3.* and TensorFlow v1.0+, making it accessible for researchers and developers working in meta-learning and few-shot learning. Users can access data preparation instructions for Omniglot and MiniImagenet, and detailed usage instructions are available within the `main.py` file.

VTIL-Core, standing for Virtual-machine Translation Intermediate Language, is a set of tools built around an optimizing compiler. Its primary purpose is binary de-obfuscation and de-virtualization, making it a valuable asset for reverse engineering and security research. Unlike other optimizing compilers such as LLVM, VTIL features an extremely versatile Intermediate Language (IL) that simplifies lifting from various architectures, including stack machines. It maintains the native ISA's concepts like the stack, physical registers, and non-SSA architecture of a general-purpose CPU, allowing native instructions to be embedded within the IL stream and physical registers to be addressed freely. VTIL also facilitates code emission back into native formats at any virtual address without file format constraints. This repository contains the core components of the VTIL Project, with further documentation and an organization website planned for its initial release.

Knowt is a comprehensive AI study tool designed to help students and teachers achieve academic success. It acts as a leading free alternative to Quizlet, offering a wide array of AI-powered features. Users can upload lecture recordings, PDFs, or videos to instantly generate detailed notes, flashcards, quizzes, and even personalized podcasts. The platform includes an AI Lecture Notetaker, AI PDF Summarizer, and tools to convert existing notes into active recall study methods. Knowt provides unlimited access to study modes like learn mode, matching games, and spaced repetition, which are often paywalled on other platforms. With a strong focus on efficiency, Knowt aims to replace traditional study methods by automating the creation of study materials, allowing students to focus more on learning and less on preparation. It also features an AP Exam Hub with study guides and practice tests for high schoolers.

Awesome-state-space-models is a comprehensive collection of research papers and repositories focused on state-space models and hybrid models. This GitHub repository serves as a centralized resource for academics, researchers, and engineers interested in the latest advancements and implementations in this field. It includes a wide array of topics, from foundational theories to specific applications in areas like language models, vision, reinforcement learning, and biomedical imaging. The collection is regularly updated with new arXiv preprints and conference papers, offering insights into various model architectures, optimization techniques, and practical use cases, including Mamba, RWKV, and other hybrid approaches.

canvas-editor is an open-source rich text editor designed for web applications, leveraging canvas and SVG for rendering. It offers a comprehensive suite of rich text operations, including undo/redo, font styling, alignment, and list management. Developers can easily insert various elements such as tables, images, links, code blocks, page breaks, and mathematical formulas. The editor also supports printing to picture and PDF, controls like select, text, date, radio, and checkbox, and features like context menus, shortcut keys, drag and drop functionality, headers, footers, page numbers, page margins, watermarks, pagination, and comments. It is ideal for creating custom text editing experiences within web applications.

caffe-yolo offers a Caffe implementation of the YOLO (You Only Look Once) real-time object detection system. This tool specifically supports YOLO v1 and includes batch normalization layers. The Caffe models used are not trained within Caffe but are converted from Darknet's original .weight files, ensuring compatibility and leveraging existing pre-trained models. The conversion process involves creating .prototxt files from Darknet's .cfg files, initializing the Caffe network, reading weights from Darknet, and then replacing initialized weights with the pre-trained ones. It provides scripts for creating .prototxt and .caffemodel files, and a main script for performing object detection on images. This makes it a valuable resource for developers and researchers working with object detection in a Caffe environment.

Mimo is an interactive learning platform designed to teach programming languages and software development skills. It offers comprehensive courses in Python, JavaScript, HTML, CSS, React, SQL, and Swift, catering to beginners and those looking to advance their coding knowledge. The platform provides step-by-step guidance, hands-on practice, and opportunities to build real-world projects. Mimo leverages AI for personalized feedback and hints, adapting to user progress to help overcome challenges. It also focuses on career-oriented learning, enabling users to build portfolios, earn certificates, and participate in live sessions to prepare for a career in software development. With a gamified approach, Mimo makes learning engaging and accessible.

pgmpy is an open-source Python library designed for causal and probabilistic reasoning through graphical models. It offers comprehensive implementations of data structures for various models including DAGs, PDAGs, MAGs, PAGs, Bayesian Networks, Dynamic Bayesian Networks, and Structural Equation Models. The toolkit includes algorithms for key tasks such as causal discovery, causal identification, causal and probabilistic inference, model validation, parameter estimation, and simulations. Its modular and extensible API ensures compatibility with scikit-learn, allowing direct use, integration into sklearn pipelines, or building higher-level tools. pgmpy supports both discrete and linear Gaussian data, as well as mixture data with arbitrary relationships.

Luos-engine is an open-source, lightweight library designed to manage hardware products as a collection of independent software features. It functions as a real-time orchestrator for cyber-physical systems, facilitating the design, testing, and deployment of embedded applications and digital twins. The tool can be utilized on any microcontroller or computer, across various networks, promoting free and fast development of multi-electronic-board connected products. By using Luos-engine, developers can leverage existing work, accelerate time-to-market, and ensure robustness and universality of their applications. It supports development, debugging, validation, monitoring, and management from anywhere, promoting organized and effective development practices for scalability and adaptability.

MiniRacer provides a minimal, modern embedded V8 JavaScript engine for Ruby, serving as an alternative to the no-longer-maintained therubyracer. It offers a simple two-way bridge, allowing Ruby applications to execute JavaScript snippets in a shared context. Key features include the ability to attach global Ruby functions to JavaScript contexts, return binary data as Uint8Array, and support for GIL-free JavaScript execution, enabling parallel script processing. It also includes timeout and memory softlimit support, rich debugging with file names in stack traces, and fork safety for web servers. Contexts can be thread-safe and created with pre-loaded snapshots for efficiency, which can also be persisted to disk. Users can control memory usage and set V8 runtime flags for experimental features or performance tuning.