Data & Analytics

SnapSite is a free and open-source browser extension designed to capture complete web pages and entire websites for offline access. It saves content as offline-ready ZIP files, ensuring that all assets, including full source code, images, fonts, and animations, are perfectly preserved. The tool offers two capture modes: single page snapshot for exact visual preservation, and full site crawl to archive up to 500 pages from a domain. SnapSite is capable of capturing complex elements like Shadow DOM components, CSS animations, and form states, making it a robust solution for web archiving, reference, or offline development. It also strips tracking scripts and ensures zero broken links for a truly offline experience.

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.

Marigold Depth Completion is an AI tool designed to generate detailed depth maps by combining an input image with sparse depth data. Users provide an image and a corresponding sparse depth map file, typically in a numpy format, to produce a comprehensive depth map. This application is particularly useful for tasks requiring accurate 3D scene understanding, such as in computer vision, robotics, and graphics processing. Developed by the Photogrammetry and Remote Sensing Lab of ETH Zurich, it offers a robust solution for enhancing depth information from incomplete datasets, making it a valuable resource for researchers and developers working with 3D data.

Qwen-VL Object-Detection is an AI tool hosted on Hugging Face Spaces, designed for comparing different Qwen-VL models in object detection tasks. Users can upload an image and define the objects they wish to detect within it. The tool then processes the image, providing an annotated output with bounding boxes around the identified objects and their corresponding labels. This functionality is particularly useful for evaluating the performance and accuracy of various Qwen-VL models, making it a valuable resource for researchers, developers, and data scientists working with computer vision and object recognition. The platform is accessible via a web interface, offering a straightforward way to interact with the models.

MONAI Label is an intelligent open-source image labeling and learning tool designed to reduce the time and effort of annotating new datasets, particularly for medical imaging. It allows users to create annotated datasets and build AI annotation models for clinical evaluation. The tool operates as a server-client system, facilitating interactive medical image annotation through AI, and can run locally on a machine with single or multiple GPUs. It supports various medical imaging modalities and integrates with popular viewers like 3D Slicer, OHIF, QuPath, and CVAT. MONAI Label also provides a framework for developing and deploying custom labeling apps, offering compositional and portable APIs for easy integration into existing workflows.

Web Bench Leaderboard is a comprehensive Data & Analytics tool hosted on Hugging Face Spaces, designed for evaluating and comparing language models. Users can access a dynamic leaderboard to view existing evaluations, filter data, and select specific columns to display relevant information about various models. The platform also enables users to submit their own evaluations, contributing to a growing dataset for performance analysis. This tool is ideal for researchers, data scientists, and anyone interested in monitoring and benchmarking the capabilities of AI language models.

Streem offers a comprehensive media intelligence solution, providing real-time monitoring and insights across various media channels. Users can track online, broadcast, social, and traditional media, with news available within seconds of publishing or broadcast. The platform boasts extensive coverage, from regional radio to global mastheads, and offers completely customizable reporting tailored to organizational needs. A key differentiator is its exclusive access to Sky News Australia coverage, including alerts, transcripts, and downloads. Streem is designed for communications, PR, and corporate affairs professionals, featuring an intuitive, easy-to-use interface and backed by 24/7 Australian account management and local specialists who understand the media landscape.

Pin Drop is a comprehensive Data & Analytics tool designed to transform maps into dynamic workspaces for planning, collaboration, and memory. It enables individuals and teams to organize locations, align in real-time, and manage work across diverse sites. Key features include shared map workspaces, location-tied tasks and notes, route and visit planning, and robust permissions for controlled sharing. The platform supports importing and exporting existing data, making it versatile for various industries like construction, logistics, and field services. Pin Drop aims to streamline operations by providing a live, visual workspace where updates, tasks, and progress are automatically synced, reducing manual effort and ensuring everyone has an up-to-date view of operations.

ESM-Variants is an AI tool designed for visualizing protein mutation scores and analyzing genetic variations. Users can select a protein by its UniProt ID, and the application generates an interactive heatmap displaying mutation scores. A key feature is the ability to optionally overlay ClinVar annotations, providing valuable context for understanding the clinical significance of specific mutations. This tool is particularly useful for researchers and scientists in the field of genomics and proteomics who need to quickly assess and interpret the impact of protein variants. It is hosted on Hugging Face Spaces and is available for free under a CC-BY-NC-4.0 license, making it accessible for academic and non-commercial research.

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.

CVPR-2019-Paper-Statistics is an open-source project offering detailed statistics and visualizations for papers accepted at the CVPR 2019 conference. Inspired by ICLR2019-OpenReviewData, this tool analyzes the acceptance rate trends from 2015 to 2019, highlighting the significant increase in paper submissions and the corresponding decrease in acceptance rates. It also provides insights into the most frequent keywords in accepted papers, such as 'Image', 'detection', '3d', 'object', 'video', 'segmentation', 'adversarial', 'recognition', and 'visual'. The project includes Jupyter Notebook code for analysis and visualization, supporting both CSV and website data formats, and requires Python 3.5 with libraries like selenium, wordcloud, and matplotlib.

WaifuDiffusion v1.4 Tags is an AI tool designed to analyze and tag images, specifically optimized for WaifuDiffusion v1.4. Users can upload an image to receive detailed tags, ratings, and character labels, making it highly suitable for booru websites and similar image-sharing platforms. The tool offers flexibility by allowing users to adjust thresholds and select different models to achieve more accurate and customized results. This capability ensures that the tagging process can be fine-tuned to meet specific requirements, providing a robust solution for image annotation and categorization.

Bioclip Demo is an interactive application hosted on Hugging Face Spaces, designed for running BioCLIP inference on images of living organisms. Users can upload a picture and either select a taxonomic level (e.g., genus, species) or provide custom class names. The tool then returns the most likely names along with confidence scores, making it valuable for visualization, data exploration, and biological research. It supports tasks such as zero-shot image classification, aiding in the identification and categorization of species based on visual input. This demo is part of the HDR Imageomics Institute's efforts to make advanced AI models accessible for scientific applications.

Skwad is a privacy-first budgeting app designed to help users understand their spending and manage personal finances without the need to link bank accounts directly. It achieves this by syncing transactions through bank email alerts, ensuring no password sharing is required. Key features include budgeting with rollovers and reports, a receipt scanner, automatic conversion of bank statement PDFs to Excel, and shared accounts for couples. Users can also import old transactions via CSV, OFX, or QIF files, and optionally link banks using Plaid or MX. Skwad emphasizes security and privacy by not requiring bank logins for its primary email scan method, offering instant transaction processing and customizable categorization.

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.

MotionBench Leaderboard is an open-source platform designed for the evaluation and comparison of various motion models. Users can submit their model evaluation JSON files to the leaderboard, which then allows for comprehensive analysis and benchmarking. The platform provides functionalities to view and filter the leaderboard data based on different evaluation dimensions, making it easy to track progress and identify top-performing models. Additionally, users have the convenience of downloading the entire leaderboard as a CSV file for further offline analysis or integration into other systems. This tool is ideal for researchers and developers in the AI community who need a standardized way to assess and compare the performance of their motion-related AI systems.

Dataset-viewer is a high-performance, AI-generated dataset viewer built with Tauri, React, and TypeScript. It excels at handling massive datasets, offering instant opening of files exceeding 100GB through virtualized rendering. Users can perform real-time searches with millisecond speed and highlighting across large files, and directly browse ZIP/TAR archives without extraction. The tool supports multiple protocols including WebDAV, SSH/SFTP, SMB/CIFS, S3, Local Files, and HuggingFace Hub, along with various formats like Parquet, Excel, CSV, JSON, and code files with syntax highlighting. Its modern interface includes dark/light themes and multi-language support, making it ideal for data scientists, log analysis, and remote data access.

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.

KPI Dashboard is a powerful data visualization tool designed to present financial data and key performance indicators (KPIs) in a clear and concise manner. Developed by Vizro, this Hugging Face Space application showcases financial data for Cumulus Financial Corp. for the fiscal year 2019. Users can explore the data through various interactive charts and tables, providing an executive-level view of critical business metrics. The dashboard is ideal for monitoring business performance, identifying trends, and making data-driven decisions. Its interactive nature allows for detailed exploration, making complex financial information easily digestible and actionable.

Rodion Novikov, a marketer and builder, has created an innovative app blocker called e4c5. This tool is designed to help users reduce their screen time by requiring them to solve chess puzzles to access blocked applications. The core idea is to transform the act of opening a distracting app into an opportunity for mental exercise and skill improvement. By choosing which apps to block, users can gain better control over their digital habits, leading to less scrolling and potentially a higher chess ELO. The app aims to provide a unique and engaging way to manage digital well-being.

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.

DeepDanbooru is an AI-based multi-label image classification system specifically designed for anime-style girl images. Built with TensorFlow, it provides a robust solution for estimating tags on visual content. The system is open-source and available on GitHub, allowing developers and researchers to access and modify its codebase. Users can prepare their own datasets or utilize tools like DanbooruDownloader to acquire data. It supports creating training projects, downloading tags from Danbooru, filtering datasets, and training custom models. The tool is ideal for those looking to categorize and analyze large collections of anime imagery with AI-driven tagging.

Invoice Extractor is a powerful AI tool designed to streamline the process of extracting data from invoices. Users can simply upload an invoice image and then interact with the system by asking questions to retrieve specific details. This capability makes it highly efficient for financial document processing and automating various accounting tasks. The tool is particularly useful for reducing manual data entry and improving accuracy. A key feature is its ability to interpret invoices in multiple languages, broadening its applicability for businesses operating internationally or dealing with diverse suppliers. It provides a user-friendly interface as a Hugging Face Space, making it accessible for quick deployment and use.

YOLOv13 is an open-source implementation for real-time object detection, leveraging hypergraph-enhanced adaptive visual perception. It introduces HyperACE for exploring high-order correlations between pixels in multi-scale feature maps and FullPAD for fine-grained information flow and representational synergy across the entire detection pipeline. The tool also incorporates model lightweighting via DS-based Blocks, replacing large-kernel convolutions with depthwise separable convolutions for faster inference without sacrificing accuracy. YOLOv13 is available in Nano, Small, Large, and X-Large variants, offering cutting-edge performance and efficiency for various object detection tasks. It supports deployment on platforms like Huawei Ascend and Rockchip, and includes a FastAPI REST API.