Research & Education

algorithmic_trading_book is a GitHub repository offering comprehensive resources for individuals interested in algorithmic trading. It includes two distinct books: 'Successful Algorithmic Trading' and 'Advanced Algorithmic Trading'. Each book is provided in PDF format and is accompanied by its corresponding source code, allowing users to study the theoretical concepts and immediately apply them through practical examples. The repository is designed to support learning and implementation of various algorithmic trading strategies, catering to both beginners looking to understand the fundamentals and more experienced traders seeking advanced techniques. All materials are open source, making them freely accessible for educational and development purposes.

EpipolarPose is a PyTorch implementation for self-supervised learning of 3D human pose using multi-view geometry, as presented in the CVPR 2019 paper. This tool is designed for computer vision researchers to estimate 3D human poses without the need for extensive 3D ground-truth data or camera extrinsics during training. It works by estimating 2D poses from multi-view images and then leveraging epipolar geometry to derive 3D poses and camera geometry, which are subsequently used to train a 3D pose estimator. In the testing phase, it can produce a 3D pose result from a single RGB image. The project includes scripts for training and validation, data preparation utilities, and pre-trained models on datasets like Human3.6M and MPII.

XRNeRF is an open-source, PyTorch-based toolbox specifically designed for Neural Radiance Field (NeRF) research and development. As part of the OpenXRLab project, it offers a robust framework for 3D scene reconstruction and novel view synthesis. The toolbox supports various scene-NeRF methods like NeRF, Mip-NeRF, KiloNeRF, Instant NGP, and BungeeNeRF, alongside human-NeRF methods such as NeuralBody and AniNeRF. XRNeRF allows users to build and customize models by defining networks, embedders, MLPs, and renderers, providing flexibility for implementing new components. It includes detailed tutorials for installation, data preparation, model definition, and training/testing procedures, making it a valuable resource for researchers and developers in the field.

Efficient Teacher, developed by Alibaba, is a comprehensive open-source library designed for both supervised and semi-supervised object detection (SSOD) using the YOLO series. Built upon the YOLOv5 framework, it leverages YACS and advanced network designs to restructure key modules, enabling a single algorithm library to support training for YOLOv5, YOLOX, YOLOv6, YOLOv7, and YOLOv8. This tool is particularly beneficial for scenarios with domain differences between training and deployment, high data labeling costs, or limited labeled data. It introduces semi-supervised object detection into practical applications, allowing users to achieve strong generalization capabilities with a small amount of labeled data and a large amount of unlabeled data. Efficient Teacher also provides features like category and custom uniform sampling to quickly improve network performance in business scenarios. It offers scripts to convert YOLOv5 weights, use existing YOLOv5 datasets without format adjustments, and easily switch between different YOLO network structures via YAML configuration.

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."

FaceRecognitionApp is an open-source Android application developed by Kristian Lauszus in 2016, designed to showcase face recognition capabilities. The app implements Eigenfaces and Fisherfaces algorithms for facial recognition, leveraging the FaceRecognitionLib library for its core calculations. It provides a practical example for developers interested in integrating face recognition into Android applications. The project is released under the GNU General Public License, encouraging community contributions and modifications. It requires Android Studio, the Android NDK, OpenCV Android SDK, and Eigen3 libraries for building and running, with detailed instructions provided for both basic and advanced users who wish to modify the source code.

CLIP Score is an AI tool hosted on Hugging Face Spaces that allows users to compare an image with multiple text prompts to determine their similarity. Users can upload an image and then input various text prompts, separated by semicolons, to receive a score indicating how closely each prompt matches the visual content of the image. This functionality is particularly useful for tasks requiring the evaluation of image-text alignment, such as in research, development, and data analysis involving multimodal data. It offers a straightforward interface for quickly assessing the relevance of textual descriptions to visual information.

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.

comparevlms is a Hugging Face Space designed for comparing various Vision Language Models (VLMs). This tool enables users to evaluate and contrast the performance of different multimodal AI models across several categories, including document understanding and object detection. Users can filter models based on their size and access detailed results for each comparison. It serves as a valuable resource for research analysis, model selection, and educational purposes, offering a structured way to assess VLM capabilities.

Compare Siglip1 Siglip2 is a specialized AI tool designed for evaluating the performance of two distinct SigLIP models, SigLIP1 and SigLIP2, in zero-shot classification tasks. Users can upload an image and provide a list of labels, and the tool will process this input to show how each SigLIP model classifies the image. It then presents the top classification results for both models, enabling a direct comparison of their accuracy and confidence. This tool is particularly useful for researchers and developers working with image recognition and model evaluation, offering insights into the strengths and weaknesses of different SigLIP architectures.

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.

Command A Vision is an AI tool developed by CohereLabs, available as a Hugging Face Space, designed for advanced image analysis. Users can upload multiple images, up to 10 per message, and provide text prompts to receive comprehensive and detailed responses. This tool is built using Gradio, making it accessible and user-friendly for various computer vision tasks. It provides a platform for exploring and interacting with AI models for visual data, offering a practical solution for those needing to analyze images with textual queries.

Fathom R1 14B is an experimental math reasoning model developed by Fractal AI Research, accessible via a Hugging Face Space. It allows users to input mathematics, science, or logic questions and receive a detailed, step-by-step analysis and answer. Users can customize the response by setting parameters for both the length and creativity of the output. This tool is designed for exploring advanced problem-solving techniques and understanding complex concepts through AI-generated explanations, making it valuable for educational and research purposes in STEM fields.

Gradio Screen Recorder is a straightforward tool designed for capturing screen activity and saving it as an MP4 video. Hosted on Hugging Face Spaces, it offers a user-friendly interface where users can initiate and terminate screen recordings with dedicated 'Record Screen' and 'Stop Recording' buttons. This tool is particularly useful for quickly creating video demonstrations, tutorials, or capturing specific on-screen actions without the need for complex software installations. It leverages the Gradio framework, making it accessible and easy to integrate for developers working within that ecosystem. Users may need to grant browser permissions for screen and microphone access to utilize its full functionality.

MathSolver.top is an AI-powered platform designed to help students solve math problems, understand concepts, and improve their grades. It features a Solver Mode that provides step-by-step solutions with over 95% accuracy for college and Olympia-level math problems in just 10 seconds. The Tutor Mode uses Socratic questioning to guide users, identify weak areas, and deepen understanding, similar to a real tutor. Additionally, the Check Mode verifies answers and pinpoints mistakes. The platform also includes a Knowledge Graph that breaks down curriculum into bite-sized chunks, linking practice questions to each concept, and offers a daily study path that adapts to individual weak areas, generating personalized questions for practice.

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.

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.

myEssai is an AI-powered essay tutor designed to provide instant and highly detailed feedback on written content. Unlike generic grammar checkers, myEssai aims to offer actionable insights to significantly improve writing quality. It helps users identify grammatical errors, stylistic inconsistencies, and structural weaknesses, making it an invaluable tool for refining essays and papers. This platform is ideal for students and researchers looking to enhance their academic writing before submission, ensuring their work is polished and effective. The tool focuses on delivering real, substantive feedback to help users develop stronger writing skills.

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.

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.

PolaroidVL 1.0 Demo offers a hands-on experience with a compact vision-language AI model, allowing users to interact directly by uploading images and posing questions. This tool is designed for detailed analysis and provides answers based on the visual and textual input. It supports common image formats like JPG, PNG, and GIF, with a file size limit of up to 10MB. Hosted on Hugging Face Spaces, it serves as an accessible platform for individuals interested in experimenting with AI's capabilities in understanding and interpreting visual information combined with natural language queries. It is particularly useful for educational purposes and research experimentation in the field of AI.

Make 24 is a daily arithmetic puzzle game designed to challenge users with a new set of four numbers each day. The objective is to use each number once, along with basic arithmetic operations (addition, subtraction, multiplication, and division), to achieve the target sum of 24. The game tracks user progress, including moves, time, and streaks, encouraging daily engagement. It features a 'Shake to undo' option for convenience and allows users to export or import their progress. For those seeking a greater challenge, a hidden Hard Mode is available. The game also provides a history of past puzzles and offers a 'Practice round' that doesn't affect the user's streak.