Research & Education

PlotPilot Software is dedicated to creating applications that prioritize simplicity, innovation, and quality. The company's core mission revolves around developing software solutions that empower individuals to live their lives according to their own preferences. They adhere to guiding principles that emphasize reducing complexity to its simplest form, challenging assumptions to foster innovation, and meticulously crafting software for reliability and longevity. PlotPilot also values collaboration, working alongside partners to achieve optimal outcomes. Their development process focuses on design, build, launch, and scale, with the ultimate goal of satisfying customer needs.

3D-Occupancy-Perception is a comprehensive research resource dedicated to the field of 3D dense perception for autonomous driving. This active repository provides a systematic survey of the latest advancements, encompassing LiDAR-Centric, Vision-Centric, and Multi-Modal Occupancy Perception. It delves into core methodological issues, including network pipelines, multi-source information fusion, and effective network training. The resource also offers evaluations, detailed performance comparisons, and discussions on current limitations and future research directions. It aims to inspire further research and development in the autonomous driving community by curating and highlighting significant works in the domain.

CalcForAnything is a comprehensive platform offering a vast collection of free online calculators across various domains such as solar & energy, finance, health & fitness, math, home & living, nursing & medical, and AI & technology. Users can access instant and accurate results without the need for sign-up or fees. The platform features a diverse array of tools, from a Solar Panel Payback Calculator and Compound Interest Calculator to a Debt Snowball Calculator and Pet Lifetime Cost Calculator. It aims to provide quick answers for personal and business financial planning, health assessments, and everyday calculations, making complex computations accessible and straightforward for everyone.

Collection Dataset Explorer is an AI tool designed for exploring datasets hosted on Hugging Face. It enables users to easily navigate and view various datasets within a specific Hugging Face collection. The application provides 'Previous' and 'Next' buttons, allowing for seamless exploration of different datasets. This tool is particularly useful for researchers, data scientists, and students who need to quickly access and understand the contents of diverse datasets without extensive setup, making it a valuable resource for data visualization and analysis within the Hugging Face ecosystem.

TSFpaper is an open-source GitHub repository dedicated to providing a curated reading list of academic papers focused on Time Series Forecasting (TSF) and Spatio-Temporal Forecasting (STF). The repository organizes these papers by their respective model types, making it easier for users to navigate and find relevant research. It serves as a valuable resource for researchers, academics, and practitioners who are interested in staying updated with the latest advancements in these specialized forecasting domains. The collection aims to streamline the process of discovering key literature, fostering knowledge sharing within the scientific community.

CogVLMv1 Captionner is an AI tool designed to generate detailed, factual descriptions of uploaded images. It identifies objects, analyzes backgrounds, and details other visual elements to provide a comprehensive caption. While the current live website indicates a runtime error, the tool's intended functionality is to offer users the ability to upload an image and, if desired, customize a prompt to guide the caption generation process, resulting in a tailored description. This makes it suitable for various applications requiring precise image analysis and textual representation.

Compare Depth Models is a Hugging Face Space designed for evaluating and comparing different depth estimation models, with a particular focus on Depth Anything and its predecessors. This tool is valuable for AI researchers and computer vision engineers who need to assess the performance and accuracy of various depth models. While the live website currently shows a runtime error, the intention of the tool is to provide a visual comparison of depth outputs from different models, aiding in research and development within the computer vision domain. It serves as a practical demonstration and comparison platform for advanced depth estimation techniques.

The Chinese Open Source Heatmap is an interactive tool hosted on Hugging Face, designed to visualize the release activity of open-source models by Chinese AI labs. Users can view a heatmap that illustrates the number of models each lab has released over the past year. Beyond the pre-generated overview, the tool allows users to search for any Hugging Face organization or individual user to create a custom heatmap, providing a personalized view of their open-source contributions. This makes it a valuable resource for researchers, developers, and anyone interested in tracking the contributions and trends within the Chinese AI open-source community. The tool is straightforward to use, offering a clear and visual representation of data.

AI Spelling Bee 2025 is a free Android mobile application designed to significantly enhance users' spelling and vocabulary skills. The app leverages three distinct AI models to generate a wide variety of questions, spanning five different difficulty levels, ensuring a continuously engaging and challenging learning experience. Users receive detailed explanations for both correct and incorrect answers, which fosters a deeper understanding of words and their proper usage. This feature is crucial for effective learning and retention. Additionally, the app supports offline play, making it a convenient and accessible tool for continuous learning anytime, anywhere, without the need for an internet connection.

Check My Progress Deep RL Course is an AI education tool hosted on Hugging Face Spaces, designed to help users track their progress in a Deep Reinforcement Learning course. By simply entering their Hugging Face username, students can have their models evaluated on specific environments to see if they meet the course requirements. This application provides a straightforward way to assess skill development and ensure alignment with course objectives, making it a valuable resource for students engaged in Deep Reinforcement Learning studies. The tool is built with Gradio, offering an accessible and interactive platform for progress monitoring.

splatviz is a comprehensive, open-source Python-based interactive viewer designed for real-time editing and analysis of 3D Gaussian Splatting scenes. Utilizing the pyimgui GUI library, it enables direct manipulation of Gaussian Python objects just before rendering, offering extensive editing and visualization capabilities. Users can view multiple scenes simultaneously, either side-by-side or in a split-screen view, and evaluate Python expressions on the resulting scene. Key features include an Edit Widget for real-time manipulation of Gaussian parameters, an Eval Widget for debugging and visualizing variables, and a Camera Widget with Orbit and WASD modes for flexible scene navigation. It also supports attaching to running 3DGS training sessions for live inspection and editing.

This project offers a comprehensive stock market environment built with OpenAI Gym, designed for simulating stock trading strategies using reinforcement learning. It integrates both Deep Q-learning and Policy Gradient algorithms, allowing users to experiment with advanced AI techniques in a financial context. The tool is implemented using Keras and supports various training data, although sample data provided is for Korean stocks. It emphasizes flexibility, encouraging users to modify model architectures and features to develop their own optimized solutions. This makes it an ideal platform for researchers and developers looking to explore and refine AI-driven trading strategies.

The Deep Reinforcement Learning Leaderboard is a Hugging Face Space designed to showcase and compare the performance of various reinforcement learning models. Users can easily search for specific models using a user ID, making it simple to track their own contributions or explore others' work. The platform provides crucial performance metrics, including mean reward and standard deviation, offering a clear overview of each model's effectiveness. This tool is invaluable for AI researchers and students who need to benchmark algorithms, understand progress in the field, and identify top-performing models in deep reinforcement learning.

EmbodiedGen Text To 3D is an AI tool developed by HorizonRobotics that enables users to generate 3D models from simple text descriptions. Users can provide either English or Chinese text prompts to initiate the 3D model creation process. Additionally, the tool offers the option to include a reference image, which can guide the system in synthesizing matching images before converting the chosen image into a textured 3D model. This functionality makes it a versatile solution for quickly prototyping or generating 3D assets without extensive manual modeling. The tool is hosted on Hugging Face Spaces, indicating its accessibility and potential for community use.

CLIP Benchmarks is a specialized tool designed for evaluating the performance of CLIP models. Hosted on Hugging Face Spaces by Marqo, this application allows users to benchmark and compare various CLIP models based on their inference and retrieval capabilities. It provides detailed performance metrics, enabling users to analyze how different models perform on specific GPUs, such as A10g and T4. This tool is particularly useful for developers and researchers who need to understand the efficiency and effectiveness of CLIP models in different hardware environments, aiding in model selection and optimization for AI applications.

SUSTechPOINTS, hosted on GitHub, provides a comprehensive platform for software development, offering various plans tailored for individuals and organizations. The Free plan includes unlimited public/private repositories, Dependabot security updates, 2,000 CI/CD minutes/month, and 500MB of Packages storage. The Team plan expands on this with access to GitHub Codespaces, repository rules, multiple reviewers in pull requests, and increased CI/CD minutes and package storage. For larger organizations, the Enterprise plan adds advanced security, compliance features like SOC1/SOC2 reports, data residency options, and extensive support, making it suitable for managing complex projects and teams.

awesome-self-driving-car is a comprehensive, open-source curated list of resources dedicated to self-driving car technology. It serves as a valuable hub for developers, researchers, and students interested in autonomous vehicles, offering links to full-stack open-source projects like Apollo and Autoware, as well as essential libraries such as ROS, OpenCV, and TensorFlow. The list also includes academic courses from institutions like Udacity and MIT, alongside a vast collection of papers and blogs covering topics from HD mapping and simulation to localization, perception, planning, and control. Furthermore, it details various systems, hardware components, datasets, and benchmarks crucial for autonomous driving research and development.

Awesome-Implicit-NeRF-Robotics is a curated repository offering a comprehensive list of research papers, code implementations, and related websites focused on Implicit Representations and Neural Radiance Fields (NeRF) within the Robotics and Reinforcement Learning (RL) domains. This resource is largely based on the survey paper "Neural Fields in Robotics: A Survey." It categorizes papers into key areas such as Object Pose Estimation, SLAM, Manipulation/RL, Object Reconstruction, Physics, and Planning/Navigation, making it an invaluable resource for academics and practitioners exploring these advanced topics. The repository is actively maintained, with regular updates on new research and workshops in the field.

Client Side Oauth is a Hugging Face Space designed to demonstrate client-side authentication using Hugging Face accounts. This application enables users to securely sign in, after which it retrieves and displays their user information and the associated access token. It serves as a practical example for developers and users interested in understanding how OAuth works within the Hugging Face ecosystem. The tool is straightforward, focusing on the core functionality of authentication and data retrieval, making it useful for testing and educational purposes related to Hugging Face's authentication mechanisms.

Awesome-Referring-Image-Segmentation is a curated GitHub repository that compiles a vast collection of academic papers and datasets related to referring image segmentation. This resource is invaluable for researchers and practitioners in the computer vision domain, offering insights into traditional and interactive methods, as well as current challenges in the field. The repository is organized into sections covering datasets, challenges, traditional referring image segmentation, interactive referring image segmentation, referring video object segmentation, 3D referring segmentation, and referring image segmentation in specific domains. It is actively maintained and encourages contributions via pull requests or issue submissions, fostering a collaborative environment for advancing research in this specialized area.

CoI Agent is an AI tool designed as an online demo of the 'Chain of Ideas' research paper, available on Hugging Face Spaces. It assists users by generating detailed research ideas from a given topic. The tool provides comprehensive results, usually taking 3-4 minutes to process. While the current live website indicates a runtime error, its intended functionality is to help users explore and develop research concepts efficiently. This tool is particularly useful for those looking to quickly brainstorm and receive structured ideas for academic or professional research endeavors.

Creative Biolabs provides custom biotechnology and pharmaceutical services, focusing on the full scope of drug discovery and development. The platform offers extensive services for antibody development projects, including discovery, engineering, and custom production. Key technologies include phage display, yeast display, and single B cell sorting for binder discovery. They also provide services for antibody characterization, immunogenicity analysis, and property optimization like affinity maturation and stability improvement. Additionally, Creative Biolabs offers custom manufacturing for membrane proteins, virus-like particles, and recombinant antibodies, ensuring high-quality solutions for various research and therapeutic needs.

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.

YOLO ARENA is a powerful tool hosted on Hugging Face designed for comparing the performance of leading object detection models. Users can upload any image and fine-tune detection strictness by adjusting confidence and Intersection over Union (IoU) sliders. The application runs five pre-trained YOLO models (v8, v9, v10, v11, and RF-DETR) on the uploaded image, providing a direct comparison of their detection capabilities. This allows developers and researchers to evaluate and benchmark different object detection algorithms efficiently, making it an invaluable resource for understanding model strengths and weaknesses in various scenarios.