Research & Education

End-to-end-Autonomous-Driving is an Open Source repository designed to be a comprehensive resource for researchers and students in the field of autonomous driving. It offers a wealth of information, including learning materials for beginners, workshops, talks, and an extensive collection of academic papers. The platform also provides details on various benchmarks, datasets, competitions, and challenges relevant to end-to-end autonomous driving. This resource aims to support the community by consolidating essential information and fostering collaboration in this rapidly evolving domain, covering topics from sensor input to vehicle motion plans.

YOLO26 vs RF-DETR is a Hugging Face Space designed for comparing the performance of two prominent object detection and segmentation models: YOLO26 and RF-DETR. Users can upload an image and then choose between detection or segmentation tasks. The tool provides options to adjust settings such as confidence threshold and model size, allowing for a detailed analysis of how each model performs under different conditions. This application is particularly useful for AI researchers and computer vision developers who need to benchmark and understand the nuances of these models in a practical, visual environment.

Cellpose is a generalist AI algorithm designed for cellular segmentation, applicable across various cell types and imaging modalities. Users can upload image files such as PNG, JPG, or TIF, and the application will process them to generate precise outlines of cells. Beyond static segmentation, Cellpose also provides flow images, which are useful for visualizing and analyzing cell movement. This tool is built using Gradio and is available under the BSD-3-Clause-Clear license, making it accessible for a wide range of research and analytical purposes in biology and related fields.

Check My Progress Audio Course is an AI tool built with Gradio, intended to help users track their progress in audio courses. This tool aims to provide a mechanism for self-assessment and reinforcement of learning, duplicating functionality found in similar projects like ThomasSimonini/Check-my-progress-Deep-RL-Course. While the concept is to assist students in monitoring their educational journey through audio content, the current live website indicates a runtime error, suggesting it is not operational at this time. It is hosted on Hugging Face Spaces by MariaK.

Splatt3R is the official implementation of a research project focused on zero-shot Gaussian Splatting from uncalibrated image pairs. This feed-forward model is designed to directly predict 3D Gaussians from standard images, eliminating the need for complex calibration processes. It is particularly useful for computer vision and 3D graphics applications where rapid 3D scene reconstruction from minimal input is critical. The tool provides an initial codebase, a research paper, a project webpage, and a Gradio demo for easy experimentation. Users can set up an Anaconda environment, compile CUDA kernels, and utilize pretrained models and data from ScanNet++ to train their own models or generate 3D scene representations.

Z3D E621 Convnext Space is a Hugging Face Space designed to analyze images and provide relevant tags. Users can either upload an image or capture one directly through the application. The tool then processes the image using a Convnext model and returns a comprehensive list of tags, each accompanied by a confidence score. This functionality is particularly useful for organizing image libraries, enhancing searchability, or understanding the content of an image through automated tagging. It offers a straightforward interface for quick image analysis.

Number Recognizer is an AI tool hosted on Hugging Face that specializes in recognizing digits from images of house or door plates. Users can easily upload a picture containing a house or door number, select a preferred model checkpoint, and the application will quickly process the image to read the displayed digits. The tool then returns the recognized number as plain text, along with a status indicating the recognition outcome. This application is useful for tasks requiring automated number extraction from real-world images, offering a straightforward solution for digit recognition.

Obooko is a comprehensive platform dedicated to providing free, legally licensed eBooks, novels, and textbooks for instant download. Users can access a wide array of fiction and non-fiction titles in PDF, EPUB, and Kindle formats, or read them directly in the Obooko Reader. The platform partners with authors and publishers to offer direct downloads without paywalls or third-party mirrors. It caters to a global audience, offering English-language titles across various genres including romance, thrillers, classics, and YA. Users can create a free account to build wishlists, rate titles, and receive recommendations, with reading progress synced across multiple devices.

Awesome_Prompting_Papers_in_Computer_Vision is a comprehensive, curated list of research papers focusing on prompt-based techniques within the fields of computer vision and vision-language learning. This resource is designed to help researchers and practitioners stay abreast of the rapidly evolving advancements in visual prompting. It categorizes papers into key areas such as Vision Prompt, Vision-Language Prompt, Language-Interactable Prompt, and Vision-Language Instruction Tuning. Each entry typically includes links to the paper and often to associated code, making it a valuable hub for exploring foundational models, parameter-efficient adaptation, and multimodal learning approaches.

Market Price Simulator is a browser-based trading sandbox designed for exploring financial market dynamics. Users can create multiple traders, place buy and sell orders, and observe how trades are automatically matched and prices evolve in real time. This simulator provides a visible order book and a history of trades, making it an ideal platform for understanding price formation, supply and demand, and order volume without financial risk. It's a valuable resource for students, researchers, and anyone interested in the mechanics of financial markets.

easy-few-shot-learning is a comprehensive open-source GitHub repository designed to simplify few-shot learning for image classification. It provides ready-to-use code and tutorial notebooks, making it accessible for both newcomers to the field and experienced practitioners seeking reliable implementations. The repository features 11 state-of-the-art few-shot learning methods, including Prototypical Networks, SimpleShot, and FEAT, along with tools for data loading tailored for few-shot classification tasks. It also includes scripts to reproduce benchmarks and utilities for research. The project supports various datasets like CU-Birds, tieredImageNet, miniImageNet, and Danish Fungi, with clear instructions for download and usage.

scienceOS is an AI research agent designed for scientists with high standards and limited time, aiming to accelerate research and collaboration. It features an AI Science Chat that can answer scientific questions by accessing over 230 million papers, a Multi-PDF Chat allowing users to upload up to 4,000 PDFs to extract data and draft manuscripts, and an AI Project Manager for organizing knowledge, sources, and findings. The tool is GDPR-compliant, with all data stored on servers within the European Union, and it explicitly states that user-generated data is not used to train its AI models. scienceOS offers both free and paid plans, with the Angel plan providing unlimited access to advanced features like deep research mode and PDF+ extraction for figures and tables.

Algorithmic-trading-python is a comprehensive open-source repository designed to accompany freeCodeCamp's YouTube course on algorithmic trading in Python. It offers practical resources for individuals looking to understand and implement algorithmic trading strategies. The repository guides users through fundamental concepts, API basics, and the development of various trading models. Key sections include building an equal-weight S&P 500 index fund, as well as quantitative momentum and value investing strategies. This resource is ideal for students and developers who want to gain hands-on experience in financial programming and automated trading.

Quizlet is an AI-enhanced mobile learning platform designed to help students master any subject. The platform provides interactive study tools, including digital flashcards and personalized practice tests, to boost comprehension and retention. Users can create their own custom study sets tailored to their specific needs or explore a vast library of community-generated content covering a wide range of topics. Quizlet aims to make learning more engaging and effective by leveraging AI to adapt to individual learning styles and provide targeted practice, making it a valuable resource for students looking to improve their academic performance.

ML-GCN is a PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, as presented in a CVPR 2019 paper. This open-source project provides researchers and developers with the code and pre-trained models necessary to apply GCNs to multi-label image recognition tasks. The implementation highlights improvements achieved by replacing Global Average Pooling (GAP) with Global Max Pooling (GMP) for feature aggregation, demonstrating enhanced performance on datasets like COCO, NUS-WIDE, and VOC2007. It includes detailed instructions for setting up requirements, downloading models, and running demos for VOC 2007 and COCO 2014 datasets, making it a valuable resource for academic research and practical application in computer vision.

FAQ_Of_LLM_Interview is a comprehensive GitHub repository designed to assist candidates in preparing for interviews in large language model (LLM) algorithm roles. It compiles a wide range of common interview questions, detailed answers, and in-depth concept analyses relevant to LLMs. The resource also covers essential knowledge areas crucial for various AI positions, making it a valuable tool for anyone looking to enhance their understanding and readiness for technical interviews in the rapidly evolving field of large language models and algorithms.

BioMedIA is an AI tool hosted on Hugging Face Spaces, designed to facilitate the exploration of AI applications within the biomedical field. While the live website indicates a build error, its intended purpose is to serve as a platform for understanding how AI can be applied in biomedical research and educational contexts. The tool is available for free, making it accessible for a wide range of users interested in the intersection of AI and biomedicine. It is suitable for researchers, students, and healthcare professionals who wish to delve into the capabilities and potential of AI in this specialized domain.

Speeko AI Speech Coach is designed to build public speaking skills, helping users become confident and effective speakers. The app tracks voice and speech patterns in real-time, providing simple, friendly alerts and personalized feedback on pace, eloquence, intonation, sentiment, talk time, and word choice. It offers bite-sized exercises tailored to unique speaking styles and goals, allowing users to practice and improve in just a few minutes a day. Speeko also includes a complete toolkit with digital notecards, interview prompts, and vocal warm-ups. Users can access exclusive content from America's #1 voice coach, Roger Love, and even work 1:1 with a coach. The tool is trusted by over 400,000 professionals and is available for Mac, iPhone, and iPad.

Chat Answer is an open-source AI dialogue client specifically designed to facilitate problem-solving on mobile devices. It integrates an AI assistant directly into the user's mobile experience, aiming to offer quick and easily accessible solutions to a wide range of problems. The tool's open-source nature suggests community-driven development and transparency, focusing on bringing AI assistance to users wherever they are.

pointnerf is an open-source implementation of Point-NeRF, a method for modeling radiance fields using neural 3D point clouds with associated neural features. This tool enables efficient rendering by aggregating neural point features near scene surfaces through a ray marching-based pipeline. A key differentiator is its ability to be initialized via direct inference of a pre-trained deep network to produce a neural point cloud, which can then be finetuned for visual quality surpassing NeRF with significantly faster training times. pointnerf also integrates with other 3D reconstruction methods and manages errors and outliers through a novel pruning and growing mechanism, making it suitable for various research applications in computer vision and graphics.

Llama 2 13b Chat is an AI chatbot specifically developed for conversational AI applications. It focuses on natural language processing and research, making it a valuable tool for those working in these fields. The chatbot is well-suited for the development and experimentation of AI chatbots, providing a platform for building and testing conversational agents. It is available for free, offering an accessible option for developers and researchers.

torch-ngp offers a PyTorch CUDA extension implementation of instant-ngp, supporting both Signed Distance Functions (SDF) and Neural Radiance Fields (NeRF). It includes a graphical user interface (GUI) for training and visualization. The repository also features PyTorch implementations of TensoRF, adapted to instant-ngp's NeRF framework, and CCNeRF for compressible-composable NeRF via rank-residual decomposition. Additionally, it provides an implementation of D-NeRF for dynamic scenes and experimental features like text-guided NeRF editing. The tool supports various datasets and offers options for different backbones and optimization techniques, making it a versatile platform for neural graphics research.

VMamba is an open-source visual state space model that transplants the Mamba state-space language model into a vision backbone, offering linear time complexity for computer vision tasks. At its core, VMamba utilizes Visual State-Space (VSS) blocks with a 2D Selective Scan (SS2D) module, which efficiently gathers contextual information from 2D vision data by traversing along four scanning routes. This design helps bridge the gap between 1D selective scan and non-sequential 2D data. The tool provides a family of VMamba architectures, accelerated through architectural and implementation enhancements. It demonstrates promising performance across diverse visual perception tasks such as ImageNet-1K classification, COCO object detection, and ADE20K semantic segmentation, showcasing its efficiency in input scaling compared to existing benchmark models. VMamba is designed for researchers and developers in the AI and computer vision fields.

drl-zh, or "Deep Reinforcement Learning: Zero to Hero!", offers a comprehensive and hands-on course designed to teach deep reinforcement learning. The curriculum is divided into two main parts: foundational concepts, where users build algorithms like DQN, SAC, and PPO from scratch, and advanced topics, which delve into areas such as curiosity-driven exploration, AlphaZero, and Reinforcement Learning with Human Feedback (RLHF). The course emphasizes learning by doing, with practical exercises ranging from playing Atari games and training robots to fine-tuning Language Models and implementing self-play with MCTS. It's structured around interactive Jupyter notebooks, providing guided TODO sections and complete solutions for reference. The entire experience is optimized for a VS Code environment, with a Dockerized setup for quick and reproducible development.