Research & Education

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.

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.

Video Depth Anything is an AI tool designed to process input videos and generate corresponding depth videos. This application visualizes the depth information present in each frame of a video, making it suitable for various applications such as 3D video reconstruction, visual effects creation, and AI research. Users can easily upload their video files and customize settings like resolution and frames per second (FPS) to achieve desired outputs. The tool is hosted on Hugging Face Spaces, indicating its accessibility and potential for community-driven development and use. Its primary function is to provide a clear, frame-by-frame depth map, offering a foundational component for advanced video analysis and manipulation.

TheWell is a data visualization tool hosted on Hugging Face Spaces, designed for exploring and visualizing physics simulation datasets. Users can select a dataset, a specific field within that dataset, and a file to view the corresponding data. A key feature is the ability to adjust time steps, which is particularly useful for analyzing dynamic fields within the simulations. This tool is ideal for researchers, students, and data scientists working with physics simulation data, offering an intuitive interface for data exploration and analysis directly within the Hugging Face ecosystem. It simplifies the process of interacting with complex scientific datasets.

GoMim Math AI is a versatile online AI math solver and homework helper that provides instant, step-by-step solutions for a wide range of mathematical problems, from basic arithmetic to advanced calculus. Users can input problems by snapping a picture of the math question or typing it in, eliminating tedious manual entry. The tool offers detailed breakdowns to help users understand the methodology, acting as a personal AI math tutor. It supports various topics including algebra, geometry, calculus, and statistics. GoMim operates on a freemium model, offering daily free credits and seamless syncing between mobile and desktop devices, making it an accessible and comprehensive study aid for students.

Zero Shot Text Classification is an AI tool hosted on Hugging Face Spaces by datasciencedojo, designed for classifying text into predefined categories without requiring specific training data for those categories. Users can easily input a piece of text and provide a list of candidate labels or categories. The tool then processes the input and returns a score for each category, indicating how well the text fits into that particular classification. This makes it a highly flexible and efficient solution for quick text categorization tasks, eliminating the need for extensive dataset preparation and model training.

Budgerigar Gender Determination is an AI tool hosted on Hugging Face designed to automatically identify the gender of budgerigars. Users can upload photos or videos of their birds, and the application will analyze the cere color to determine gender. The tool then draws labeled boxes around each detected bird, indicating its gender. It offers adjustable confidence and detection settings, allowing users to fine-tune the analysis. This free tool provides a quick and easy method for budgerigar owners, bird enthusiasts, and researchers to determine the gender of their birds without manual inspection.

Awesome-Self-Supervised-Papers is a comprehensive, open-source repository on GitHub dedicated to collecting and organizing research papers in the fields of self-supervised learning and representation learning. It serves as a valuable resource for researchers and practitioners, offering a curated list of academic publications. The repository is regularly updated with new papers, including those focusing on self-supervised learning with distillation and dense prediction. It categorizes papers by areas such as Computer Vision (CV) pretraining, contrastive learning, image transformation, self-supervised learning with knowledge distillation, and various other methods, providing details like conference/journal, ImageNet accuracy, and other performance metrics where applicable. Contributions to the paper bank are welcomed.

Open Knowledge Maps is the world's largest AI-based search engine for scientific knowledge, designed to transform research discovery. It offers a visual overview of research topics, helping users find relevant research outputs and identify key concepts. The platform is a charitable non-profit organization committed to open science principles, aiming to create an inclusive and sustainable infrastructure. It uses AI to analyze article metadata from trusted providers like PubMed and BASE, clustering resources and generating area titles to provide a comprehensive and interactive map of scientific literature. Users can explore topics, identify themes, and focus on pertinent papers, making it an invaluable tool for researchers and academics.

Coglayer is an application designed to make learning more accessible and enjoyable by providing personalized text and audio content. Users have the flexibility to customize the length of the content they receive, tailoring it to their specific learning needs and preferences. A key feature of Coglayer is its ability to generate clarifying questions, which helps users deepen their understanding and refine their learning process. The platform supports various modes of interaction, including reading, listening to, downloading, and sharing content, making it a versatile tool for different learning styles. While the website currently shows a redirect, the core functionality focuses on adaptive content delivery to improve educational outcomes.

AI Detector By Custom Writings is a precise AI content detector designed to identify if any part of a text was AI-generated. It supports text analysis from pasted content or uploaded documents in formats like DOC, TXT, PDF, and PowerPoint presentations. The tool is useful for academic works, SEO texts, and documentation, and is also available as a browser extension for real-time content monitoring. While no AI detector can achieve 100% accuracy, this tool aims for high precision, especially with texts over 275 words. It addresses concerns about data privacy and security, ensuring user input remains confidential. The developers continuously update the model to keep pace with rapidly evolving AI technologies, making it a reliable choice for preventing academic dishonesty and verifying content originality.

GENTRL, or Generative Tensorial Reinforcement Learning, is an open-source model designed to accelerate the identification of potent molecular inhibitors. It functions as a variational autoencoder with a sophisticated prior distribution of the latent space, utilizing tensor decompositions to encode relationships between molecular structures and their properties, even with missing data. The model trains in two stages: initially mapping a chemical space onto a latent manifold, then freezing parameters to explore the chemical space for molecules with high reward. This approach supports research in areas like identifying DDR1 kinase inhibitors, making it a valuable tool for academic and pharmaceutical research.

Dexa is an innovative platform designed to unlock expert knowledge by providing direct answers from trusted professionals featured in various podcasts. Users can ask anything and get insights from neuroscientists, entrepreneurs, urologists, and other specialists. The platform curates content from popular podcasts like Huberman Lab, Impact Theory, and Mind Pump, allowing users to explore topics ranging from health and wellness to business and personal development. Dexa aims to make expert advice instantly accessible, functioning as a personal 'Ask Me Anything' (AMA) session with a diverse range of thought leaders. It also offers features for podcasters to amplify their impact, engage audiences, and gain insights.

Interstellar offers an interactive 3D simulation of a wormhole and a black hole, allowing users to explore these cosmic phenomena. The tool provides various controls, including the ability to adjust speed, field of view, and resolution, enhancing the user's immersive experience. Additionally, a unique teleport feature allows instant travel to the next celestial object within the simulation. Hosted on Hugging Face Spaces, Interstellar is accessible via keyboard, mouse, or touch controls, making it suitable for a wide range of users interested in scientific visualization and exploration. It serves as an engaging platform for educational or exploratory purposes, providing a visual representation of complex astrophysical concepts.

MMAction2 is an open-source toolbox for video understanding built on PyTorch, forming a key part of the OpenMMLab project. It features a modular design, allowing users to easily construct customized video understanding frameworks by combining different components. The toolbox supports five major video understanding tasks: action recognition, action localization, spatio-temporal action detection, skeleton-based action detection, and video retrieval. MMAction2 is well-tested and documented, providing detailed API references and unit tests, making it a robust platform for researchers and developers in the field.

MMTracking is an open-source video perception toolbox built on PyTorch, forming a key part of the OpenMMLab project. It stands out as the first open-source toolbox to unify diverse video perception tasks, including video object detection (VID), multiple object tracking (MOT), single object tracking (SOT), and video instance segmentation (VIS) within a single framework. Its modular design allows users to easily construct customized methods by combining different components. MMTracking is known for its simplicity, speed, and strength, leveraging MMDetection for detector integration and running all operations on GPUs for fast training and inference. It reproduces state-of-the-art models, often outperforming official implementations, and supports a wide range of datasets and methods for each task.

NASLib is a modular and flexible framework designed to facilitate Neural Architecture Search (NAS) research by providing a common codebase to the community. It offers high-level abstractions for designing and reusing search spaces, along with interfaces to various benchmarks and evaluation pipelines. This enables researchers to implement and extend state-of-the-art NAS methods with minimal code. The library's modular nature allows for easy innovation on individual components, such as defining new search spaces while reusing existing optimizers, or proposing new optimizers with current search spaces. Developed by the AutoML Freiburg group, NASLib is continuously updated with new search spaces, optimizers, and benchmarks.

Object-Detection-Metrics is an open-source toolkit designed to provide comprehensive metrics for evaluating object detection algorithms. It addresses the lack of consensus and standardized implementations for these metrics, offering a reliable solution for researchers and developers. The tool includes implementations for popular metrics such as Intersection Over Union (IOU), Precision, Recall, Precision x Recall curve, and Average Precision (AP), including both 11-point and all-point interpolation methods. It simplifies the evaluation process by accepting ground truth and detected bounding boxes without requiring complex file conversions. The implementation has been carefully compared against official versions, ensuring accurate and trustworthy results for benchmarking different approaches.

OpenCPU is an open-source system designed for embedded scientific computation and reproducible research using the R programming language. It exposes a simple yet powerful HTTP API for remote procedure calls (RPC) and data interchange with R, offering a reliable and scalable foundation for building statistical services or R-based web applications. The system can run as a single-user development server within an interactive R session or as a multi-user Linux stack based on Apache2. It is fully open source and permissively licensed, providing detailed documentation and example applications for both cloud server and local development installations.

Opus-MT is an open-source project offering neural machine translation models and web services, built upon Marian-NMT and trained using OPUS data. It features SentencePiece-based segmentation and guided alignment for its models. The platform provides pre-trained, downloadable translation models under a CC-BY 4.0 license, including those from the Tatoeba translation challenge. Users can set up a Tornado-based web application with a UI and API for multiple language pairs, or a simpler websocket service. While it includes scripts for training models, these are currently optimized for the University of Helsinki and CSC computing environments. Opus-MT is ideal for researchers and developers looking to integrate or build upon open translation services.

Langua - AI Language Tutor is a free mobile application available on Android, designed to enhance language learning through artificial intelligence. The app provides an interactive platform where users can engage in conversations to practice and improve their reading, listening, and speaking skills. It features custom scenes and avatars within a chat-like interface, simulating real-world dialogues to build confidence and fluency in multiple languages. This tool aims to make language acquisition more engaging and accessible for learners.

AI Article Summarizer is a free online tool designed to quickly condense lengthy texts such as academic research, journal essays, and news stories into concise summaries. Users can upload files, including PDFs up to 30 MB, or paste text directly to extract key points effortlessly. The platform boasts fast analysis, processing articles in approximately 5 seconds, and provides accurate results by focusing on the core content. It supports over 80 languages, including English, Spanish, German, and French, making it accessible to a global audience. A unique feature is the real-time AI chat, allowing users to ask follow-up questions for deeper analysis and elaboration on the summarized content. The tool emphasizes secure file handling and easy navigation, catering to students, teachers, and researchers.

ROLO is an open-source recurrent YOLO (You Only Look Once) model designed for simultaneous object detection and tracking. It utilizes the regression capabilities of Long Short-Term Memory (LSTM) networks to interpret visual features and translate them into precise object coordinates. This approach allows ROLO to not only detect objects within a frame but also track their movement over time, making it suitable for applications requiring continuous object monitoring. The project is available on GitHub, indicating its open-source nature and accessibility for developers and researchers.

RSL-RL is a GPU-accelerated, lightweight learning library specifically designed for robotics research. It provides a fast and simple implementation of various learning algorithms, including PPO and Student-Teacher Distillation, making it ideal for researchers to quickly prototype and test new ideas without the complexity of larger libraries. The library supports multi-GPU training for high-throughput performance and has been proven effective in numerous research publications. RSL-RL is compatible with popular robot learning environments such as Isaac Lab, Legged Gym, mjlab, and MuJoCo Playground, and can be easily installed via PyPI. Its minimal and readable codebase also offers clear extension points for customization.