Research & Education

Face_Pytorch offers an open-source implementation of various face recognition algorithms within the PyTorch framework. This project includes well-known algorithms such as ArcFace, CosFace, and SphereFace, providing a comprehensive toolkit for researchers and developers. It supports data preparation for CNN training using datasets like CASIA-WebFace and Cleaned MS-Celeb-1M, aligned by MTCNN. The project also facilitates performance testing on benchmarks like LFW, AgeDB-30, CFP-FP, and MegaFace, with detailed verification results provided for different model types and protocols. It's designed for those looking to implement and evaluate face recognition models, offering flexibility for custom dataset paths and parameters.

FSDrive is the official implementation for the research paper "FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving," which was recognized as a NeurIPS 2025 spotlight. This tool introduces a novel spatio-temporal Chain-of-Thought (CoT) approach, allowing end-to-end autonomous driving Visual Language Agents (VLA) to visually process and plan trajectories. It uniquely unifies visual generation and understanding with minimal data, marking a significant advancement in applying visual reasoning to autonomous driving. FSDrive provides comprehensive instructions for installation, data preparation, training, inference, evaluation, and visualization, making it a valuable resource for researchers and developers in the autonomous driving domain.

The Knight in the Panther's Skin is a digital edition of Shota Rustaveli's 12th-century Georgian epic poem, Vepkhistkaosani. This interactive platform provides the full text in English (Wardrop 1912 translation) with a parallel Georgian text from the critical edition, allowing for a bilingual reading experience. Users can explore all 47 chapters, access detailed annotations, and view illustrations that bring the allegorical masterpiece to life. The tool enhances engagement with this historically significant work by weaving themes of friendship, love, and devotion into an accessible digital format, making it ideal for students, scholars, and enthusiasts of medieval literature and Georgian culture.

Qwen-VL, developed by Alibaba Cloud, is a powerful open-source large vision language model (LVLM) that accepts image, text, and bounding box inputs, and outputs text and bounding boxes. It offers strong performance, significantly surpassing existing open-sourced LVLMs on multiple English evaluation benchmarks. Key features include multi-lingual support for English, Chinese, and multi-lingual conversations, end-to-end recognition of bi-lingual text in images, and multi-image interleaved conversations. It is also the first generalist model to support grounding in Chinese, allowing for bounding box detection through open-domain language expression. The model boasts fine-grained recognition and understanding with a 448x448 resolution, promoting detailed text recognition and document QA.

GeoChat is an open-source, grounded Large Vision Language Model (LVLM) specifically designed for Remote Sensing (RS) applications. Unlike general-domain models, GeoChat is tailored to handle high-resolution RS imagery and employs region-level reasoning for detailed scene interpretation. It leverages a newly created RS multimodal dataset and is fine-tuned using the LLaVA-1.5 architecture, resulting in robust zero-shot performance across various RS tasks. These tasks include image and region captioning, visual question answering, scene classification, visually grounded conversations, and referring object detection. GeoChat also introduces a novel data generation pipeline to create rich instruction sets for the RS domain, making it a valuable tool for researchers and developers in AI and remote sensing.

RoboVerse is an open-source initiative providing a unified platform, dataset, and benchmark specifically designed for scalable and generalizable robot learning. It aims to accelerate research and development in robotics and AI by offering a comprehensive ecosystem for creating, testing, and evaluating robot learning algorithms. The platform integrates various simulation frameworks and renderers, including Isaac Lab, Isaac Gym, MuJoCo, and Blender, alongside data from projects like RLBench and Maniskill. RoboVerse encourages community contributions and provides detailed documentation and tutorials to help users get started. Its focus on a standardized environment and extensive datasets makes it a valuable resource for advancing the field of robot learning.

Hyperspectral-Image-Super-Resolution-Benchmark is an open-source collection of resources dedicated to hyperspectral image super-resolution. Curated by Junjun Jiang, this benchmark provides a comprehensive list of techniques and papers for generating high spatial and high spectral resolution images. It covers four main classes of super-resolution: spatiospectral super-resolution (SSSR), spectral super-resolution (SSR), single hyperspectral image super-resolution (SHSR), and multispectral image and hyperspectral image fusion (MHF). The resource includes pioneer work, technique reviews, and recent advancements, often with links to PDF papers and code, making it an invaluable tool for researchers and academics in the field.

Research in English News is a dedicated platform designed to make the latest academic research accessible to a broader audience. It translates complex scientific papers into concise, easy-to-understand articles, covering a wide range of topics from astrophysics and quantum communication to mental health and computer vision. The website features summaries of groundbreaking studies, highlighting key findings and their implications, such as new AI models for retinal scans, advancements in autonomous system safety, and insights into strange metals. This approach democratizes access to cutting-edge scientific knowledge, allowing individuals to stay informed about significant developments without needing to navigate dense scholarly content.

What Word Is That? is a free online word counter tool that provides instant statistics for any text you paste or type. It accurately counts words, characters (including and excluding spaces), sentences, and paragraphs. Additionally, it calculates the estimated reading time based on an average adult reading speed and determines the average word length and the number of unique words. The tool operates entirely within your browser using JavaScript, ensuring that your text never leaves your device and maintaining complete privacy. It's a straightforward and efficient solution for anyone needing quick text analysis without sign-ups or page reloads, making it ideal for academic writing, blog post optimization, social media content creation, and freelance writing tasks.

Top Contributors To Follow is a web-based tool designed to identify and showcase the most impactful users on Hugging Face. It provides a ranked table of model creators based on the cumulative likes their models have received within a selected month. Users can easily pick a specific month to see who the top contributors were during that period. Each entry in the table includes the user's name, their total likes for the chosen month, and quick links to their Hugging Face profile, making it simple to discover and follow leading figures in the AI community. This tool is particularly useful for those looking to identify influential creators, explore popular models, or stay updated on key contributors within the Hugging Face ecosystem.

LightHearted AI is a Forbes-recognized precision cardiology company dedicated to preventing heart-related deaths. Their novel technology, LightScope, is a laser-based device that significantly quantifies blood flow, offering 16 times the signal-to-noise ratio compared to existing technologies. This allows for the detection of cardiovascular conditions in a mere 10 seconds by shining light on the neck, eliminating the need for an expert. The company's mission is to prevent 10 million heart-related deaths by 2030, making advanced cardiac diagnostics accessible and efficient.

Juno Research is an AI-led interview platform designed to gather deep human insights by conducting unscripted conversations with real people. This approach helps uncover information users might not have known to ask, revealing authentic thoughts, feelings, and decision-making processes. The tool aims to provide a more nuanced understanding of target audiences, going beyond traditional survey methods to capture qualitative data directly from individuals. It is particularly useful for understanding user needs, market perceptions, and behavioral drivers, making it a valuable asset for product development, marketing strategy, and overall business intelligence.

AI Phone Leaderboard is a Hugging Face Space that offers a comprehensive leaderboard for evaluating the AI performance of various mobile devices. This tool allows users to analyze benchmark results, providing insights into how different phones stack up in terms of AI capabilities. It is particularly useful for AI enthusiasts, researchers, and mobile developers who need to compare and understand the AI processing power of current mobile technology. The platform is hosted on Hugging Face, leveraging its infrastructure for accessibility and community engagement.

Bioclip 2 Demo is an interactive application hosted on Hugging Face Spaces, designed for biological research and data exploration. Users can upload images of plants, animals, or other organisms, and the tool will predict their likely taxonomic rank, such as species, genus, or family. This is achieved using a sophisticated large tree-of-life model. The demo also allows users to supply their own taxonomic tree, offering flexibility for specialized research. It serves as a valuable resource for visualization and understanding biodiversity through image analysis, making advanced biological classification accessible.

AstaBench Leaderboard offers a comprehensive platform for viewing and comparing benchmark leaderboards across diverse AI categories. Users can explore performance metrics for models in areas such as literature understanding, code execution, data analysis, and discovery. The tool is hosted on Hugging Face Spaces by AllenAI, providing a centralized location to track and evaluate the advancements in AI model capabilities. It serves as a valuable resource for researchers and developers to assess the effectiveness of different AI systems without requiring any input, simply by browsing the available leaderboards.

phd-bibliography is an open-source curated collection of academic references focusing on optimal control, reinforcement learning, and motion planning. This GitHub repository provides a structured bibliography covering a wide array of topics including Dynamic Programming, Control Theory, Model Predictive Control, Safe Control, Game Theory, Sequential Learning, Multi-Armed Bandit problems, Black-box Optimization, and various aspects of Reinforcement Learning. It is designed as a valuable resource for researchers, students, and practitioners looking for foundational and advanced literature in these complex domains. The bibliography is organized by topic, making it easy to navigate and find relevant papers, and includes links to specific works like AlphaGo and AlphaZero.

EDGS is a Hugging Face Space by CompVis that offers a simplified approach to 3D Gaussian Splatting. Users can upload a front-facing video or a folder of images of a static scene. The tool then automatically extracts frames, and runs a process to optimize the 3D scene. This tool is designed to improve the efficiency of 3D Gaussian Splatting by eliminating the need for densification, making the process more accessible and streamlined for creating 3D representations from 2D inputs. It provides a practical demonstration of the research outlined in the paper "EDGS: Eliminating Densification for Efficient Convergence of 3DGS."

DeepResearch Bench is a comprehensive platform designed for evaluating deep research agents, offering a dynamic leaderboard to track and compare their performance. Users can easily search for specific AI models or filter them by various categories to analyze their scores and effectiveness. A key feature is the ability to conduct side-by-side comparisons of two chosen models, allowing for detailed analysis of their results. This tool is particularly valuable for AI researchers and data scientists who need to assess and understand the capabilities of different deep research agents in a structured and comparative manner, aiding in model selection and performance optimization.

Scenic is an open-source JAX library developed by Google Research, specifically designed for computer vision research with a strong emphasis on attention-based models. It facilitates the development of classification, segmentation, and detection models across multiple modalities, including images, video, audio, and multimodal combinations. The library provides essential boilerplate code for launching experiments, logging, and profiling, alongside optimized training and evaluation loops. Scenic also includes input pipelines for popular vision datasets and a collection of state-of-the-art models and baselines, some developed within Scenic and others reimplemented. Its philosophy prioritizes rapid prototyping and simplicity, encouraging forking and copy-pasting for customization before upstreaming widely useful functionalities.

EMNLP 2022 Papers offers an interactive platform for exploring research papers presented at the EMNLP 2022 conference. Users can navigate a visual map to discover connections between different papers, search by title, track, or author, and access abstracts and links directly from the map markers. This tool is designed to facilitate academic research by providing an intuitive way to browse a large collection of scientific literature, making it easier to find relevant studies and understand the landscape of research topics from the conference.

Find a leaderboard is a Hugging Face Space by OpenEvals designed to help users explore and discover leaderboards from the vast Hugging Face community. This web application provides a centralized hub for viewing various leaderboards, making it easier to track and compare AI model performance. The tool is user-friendly, requiring no input; simply visiting the site displays the available leaderboards. It also features automatic dark mode switching, adapting to your system settings for optimal viewing comfort. This makes it a convenient resource for anyone interested in the latest advancements and benchmarks within the AI community.

FutureBench Leaderboard is a Hugging Face Space application developed by togethercomputer, designed for displaying and analyzing prediction leaderboard data. Users can filter the data by specific date ranges, providing flexibility in examining performance trends over time. The application offers summaries and samples of the data, enabling quick insights into the prediction models' performance. While the current live website content indicates a build error, the tool's intended functionality is to provide a web interface for exploring datasets and viewing statistics, with data downloaded from HuggingFace on startup. This makes it a valuable resource for those interested in monitoring and evaluating AI model predictions.

SOTA-MedSeg is an open-source resource that compiles state-of-the-art medical image segmentation methods, primarily focusing on challenges from MICCAI (Medical Image Computing and Computer Assisted Intervention) conferences, with updates through 2023. The repository provides an overview of various medical image segmentation challenges, detailing the segmentation target, image modality, dataset size, and the base network architecture used in winning solutions. It covers a wide range of anatomical areas including head and neck, brain, retina, heart, chest, and abdomen, addressing diverse segmentation tasks like tumor, aneurysm, and organ segmentation. The resource highlights the continued dominance of U-Net and its variants in winning solutions and includes links to papers and code for many of the listed methods.

StreamPETR is an official implementation of a research paper accepted by ICCV 2023, focusing on exploring object-centric temporal modeling for efficient multi-view 3D object detection. This open-source tool provides a robust framework for researchers and developers working in the field of computer vision and autonomous driving. Key features include support for StreamPETR, PETR, and Focal-PETR codebases, flash attention, deformable attention (RepDETR3D), and checkpoints. It also offers functionalities like sliding window training, efficient training in streaming video, TensorRT inference, and 3D object tracking. The repository provides detailed documentation for environment setup, data preparation, and training/inference procedures, along with model zoo results on NuScenes validation and test sets.