Browse AI Tools

Zymergen is an industrial biotechnology company that leverages artificial intelligence, machine learning, and automation to design, develop, and manufacture novel molecules and materials. The company's core platform combines high-throughput biology, data science, and automation to engineer microbes and biological systems for the production of high-value products. Zymergen's technology is used to accelerate the discovery and optimization of bio-based alternatives to traditional petrochemical-derived materials, enabling more sustainable manufacturing across various industries. The platform is designed for scientists and engineers in sectors such as agriculture, consumer electronics, personal care, and advanced materials. By applying AI to biological design, Zymergen aims to solve complex problems in material science and chemistry, offering a data-driven approach to innovation that reduces development timelines and costs. The company operates as a B2B enterprise, partnering with large corporations to co-develop and scale new products.

YOLOv8 is a state-of-the-art, real-time object detection model developed by Ultralytics, representing the latest iteration in the popular YOLO (You Only Look Once) series. It's designed for high-speed, accurate detection of objects in images and videos with a single forward pass through a neural network. Unlike traditional detection systems that require multiple stages, YOLOv8 processes entire images simultaneously, making it exceptionally fast while maintaining competitive accuracy. The framework supports multiple computer vision tasks beyond object detection, including instance segmentation, image classification, and pose estimation. Built on PyTorch, YOLOv8 offers a user-friendly Python package with extensive documentation and pre-trained models that work out-of-the-box. It's widely used by researchers, developers, and practitioners across industries for applications ranging from autonomous vehicles and surveillance to medical imaging and retail analytics. The model architecture balances speed and accuracy through careful design choices including anchor-free detection, advanced backbone networks, and efficient feature pyramid networks. Ultralytics provides comprehensive tools for training, validation, and deployment, making it accessible to both beginners and experts in computer vision.

Zero123++ is an advanced AI model for generating consistent 3D-consistent novel views from a single input image. Developed by SUDO AI, it builds upon the original Zero-1-to-3 architecture with significant improvements in quality, consistency, and usability. The model takes a single RGB image as input and produces multiple coherent views of the same object from different camera angles, enabling 3D reconstruction and multi-view synthesis without requiring 3D training data. It's particularly valuable for content creators, game developers, AR/VR professionals, and researchers who need to generate 3D assets from limited 2D references. The open-source implementation allows both local deployment and cloud-based inference, supporting various input resolutions and offering fine-grained control over camera parameters. Unlike traditional 3D modeling tools that require extensive manual work, Zero123++ automates the view generation process while maintaining geometric consistency across outputs.

ZoeDepth is an advanced, open-source monocular depth estimation model developed by researchers at Intel Labs and the University of Toronto. It transforms a single 2D image into a detailed depth map, effectively creating a 3D representation of the scene. Unlike earlier models that offered a one-size-fits-all approach, ZoeDepth introduces a novel multi-head architecture with separate encoders for metric and relative depth estimation, allowing it to produce highly accurate, metric-aware depth predictions without requiring camera intrinsics. It is designed for robustness across diverse scenes, from indoor environments to outdoor landscapes. The model is particularly valuable for applications in robotics, augmented reality, 3D reconstruction, and computational photography, where understanding scene geometry from a single viewpoint is critical. Its release as a pre-trained model on GitHub makes state-of-the-art depth estimation accessible to developers, researchers, and hobbyists for integration into various projects.

XVFI (eXtreme Video Frame Interpolation) is an advanced, open-source AI research project focused on generating high-quality intermediate video frames between existing ones, a process known as video frame interpolation. Developed by researchers including Jihyong Oh, it specifically targets scenarios with large motion, where objects move significantly between frames. Unlike simpler interpolation methods that assume small, linear motion, XVFI employs a sophisticated deep learning architecture to explicitly model and handle extreme motion. It is designed for researchers, developers, and video processing enthusiasts who need to increase video frame rates (e.g., converting 30fps to 60fps or higher) for applications like slow-motion generation, video restoration, and improving visual fluidity in gaming or film production. The tool is implemented in PyTorch and is primarily accessed via its GitHub repository, which provides the code, pre-trained models, and instructions for inference and training. It represents a state-of-the-art approach in a niche but technically challenging area of computer vision, aiming to produce temporally coherent and visually plausible frames even in complex scenes with occlusions and fast-moving objects.

YOLOv5 (You Only Look Once version 5) is a state-of-the-art real-time object detection system developed by Ultralytics. It represents a significant evolution in the YOLO family of models, offering improved speed, accuracy, and ease of use compared to previous versions. The framework is implemented in PyTorch and provides a complete pipeline for object detection tasks, including data preparation, model training, validation, and deployment. YOLOv5 is widely used by researchers, developers, and companies for applications ranging from autonomous vehicles and surveillance systems to industrial quality control and medical imaging. Its modular architecture supports various model sizes (n, s, m, l, x) to balance speed and accuracy requirements. The system excels at detecting and classifying multiple objects within images and video streams simultaneously with high precision and minimal computational overhead. It has become a popular choice in both academic research and production environments due to its robust performance, extensive documentation, and active community support.

YOLO (You Only Look Once) is a revolutionary real-time object detection system that frames detection as a single regression problem, directly predicting bounding boxes and class probabilities from full images in one evaluation. Developed by Joseph Redmon and Ali Farhadi, YOLO processes images at remarkable speeds (45-155 frames per second) while maintaining competitive accuracy. Unlike traditional detection systems that use complex pipelines with region proposal networks, YOLO treats detection as a unified regression task from image pixels to bounding box coordinates and class probabilities. This approach enables end-to-end training and inference, making it exceptionally fast and suitable for real-time applications. YOLO's architecture divides the input image into an S×S grid, with each grid cell predicting B bounding boxes and confidence scores for those boxes, along with C class probabilities. The system has evolved through multiple versions (YOLOv1 through YOLOv8 and beyond), each improving accuracy, speed, and capabilities while maintaining the core philosophy of unified detection. YOLO is widely used in autonomous vehicles, surveillance systems, medical imaging, retail analytics, and any application requiring fast, accurate object detection. Its open-source nature and active community have made it one of the most popular computer vision frameworks globally.

YOLACT (You Only Look At CoefficienTs) is an open-source, real-time instance segmentation model developed by Daniel Bolya and colleagues. It is a deep learning framework designed to perform pixel-level object detection and segmentation in images and video streams at high speeds, making it suitable for applications requiring immediate feedback. Unlike slower two-stage methods like Mask R-CNN, YOLACT employs a single-stage architecture that generates prototype masks and prediction coefficients in parallel, which are then combined to produce final instance masks. This approach achieves a favorable balance between speed and accuracy, enabling real-time performance on standard GPUs. It is primarily used by researchers, developers, and engineers in fields such as robotics, autonomous vehicles, video surveillance, and augmented reality, where quick and precise object delineation is crucial. The model is implemented in PyTorch and is celebrated for its simplicity, efficiency, and strong performance on benchmarks like COCO. YOLACT addresses the problem of computationally expensive instance segmentation, providing a practical solution for deploying advanced computer vision capabilities in resource-constrained or latency-sensitive environments.

Zero-1-to-3 is an open-source AI research model developed by a team from Columbia University and Google Research. It is designed to generate novel 3D views of an object from a single input image. The core innovation is a conditional diffusion model that learns the relative camera viewpoint transformation, allowing it to predict how an object would look from different angles based on just one reference photo. This addresses a fundamental challenge in 3D vision: creating a complete 3D representation from limited 2D data. It is primarily used by researchers, developers, and digital artists working in 3D content creation, augmented reality, and robotics. The model does not produce textured meshes directly but generates multi-view consistent 2D images, which can then be processed by other algorithms like NeRF or Gaussian Splatting to create full 3D assets. Its release has significantly advanced the field of single-image 3D reconstruction by providing a robust, learning-based method for viewpoint synthesis.

XceptionNet is a deep learning model specifically designed for detecting manipulated facial content in videos, commonly known as deepfakes. Developed as part of the FaceForensics++ benchmark, it serves as a state-of-the-art baseline for forensic analysis of facial forgeries. The model is built upon the Xception architecture, which employs depthwise separable convolutions to efficiently capture spatial hierarchies in visual data. Researchers and security professionals use XceptionNet to identify AI-generated facial manipulations created by various synthesis methods, including face swapping, expression transfer, and identity replacement. The tool processes video frames to classify them as authentic or manipulated, providing confidence scores for detection. It's particularly valuable for media verification platforms, social media companies combating misinformation, and forensic laboratories analyzing digital evidence. The model has been trained and evaluated on large-scale datasets containing both real videos and sophisticated synthetic forgeries, making it robust against common manipulation techniques.

Winston AI is an advanced AI content detection platform designed to identify text generated by artificial intelligence systems like ChatGPT, GPT-4, Claude, and other large language models. The tool serves educators, content publishers, academic institutions, and businesses who need to verify the authenticity of written content and ensure human authorship. It addresses growing concerns about AI-generated plagiarism, academic dishonesty, and content authenticity in educational and professional settings. Winston AI uses proprietary detection algorithms that analyze writing patterns, syntax, and semantic structures to distinguish between human and AI-generated text with reported high accuracy rates. The platform offers both web-based interface and API access, making it suitable for individual checks and integration into larger content management workflows. It's particularly valuable in educational environments where maintaining academic integrity is crucial, and in publishing/content creation industries where original human authorship needs verification.

Wonder3D is an innovative AI-powered research project that generates high-quality, textured 3D models from single 2D images in approximately 2 minutes. Developed by researchers, it addresses the significant challenge of creating detailed 3D assets from flat images, a process traditionally requiring extensive manual modeling or multi-view capture setups. The tool utilizes a novel cross-domain diffusion model to simultaneously generate consistent multi-view normal maps and color images, which are then fused to create a coherent 3D mesh with high-fidelity textures. It's primarily used by researchers, digital artists, game developers, and 3D content creators who need to rapidly prototype or create 3D assets without specialized 3D modeling expertise. The technology represents a significant advancement in single-image 3D reconstruction, producing results that maintain geometric consistency and visual quality comparable to more complex multi-view systems.

Wings 3D is an advanced, open-source 3D modeling application specializing in subdivision surface modeling techniques. Developed as a fork of the Nendo modeling software, it provides professional-grade modeling tools for creating complex 3D meshes, characters, and objects. The software is particularly popular among indie game developers, digital artists, and hobbyists who need powerful modeling capabilities without the cost of commercial software. Wings 3D focuses on polygon modeling with a clean, intuitive interface that supports various modeling operations including extrusion, beveling, cutting, and bridging. It uses a context-sensitive right-click menu system that adapts to the current selection mode, making the workflow efficient for experienced users. The application supports multiple export formats including OBJ, 3DS, and VRML, allowing integration with other 3D software and game engines. While it lacks built-in rendering capabilities, it works well as a modeling front-end for rendering packages like POV-Ray and YafaRay.

Which Face Is Real? is an educational web-based game designed to help users develop skills in identifying AI-generated synthetic faces versus real human photographs. Created by researchers Jevin West and Carl Bergstrom from the University of Washington, the tool addresses the growing challenge of deepfakes and synthetic media in the digital age. It presents users with side-by-side images—one real photo and one generated by AI models like StyleGAN—and challenges them to identify the authentic one. The primary goal is educational, aiming to improve public awareness and critical digital literacy regarding the capabilities of modern generative AI. It serves as a practical, hands-on resource for journalists, educators, students, and the general public to train their visual perception against increasingly convincing forgeries. The site explains common telltale signs of AI generation, such as irregularities in backgrounds, hair, teeth, glasses, and symmetry, turning detection into an interactive learning experience.

Virtual Staging AI is a web-based platform that uses artificial intelligence to digitally furnish and decorate empty rooms in property photos. It is primarily used by real estate agents, property managers, home stagers, and interior designers to enhance marketing materials. The tool addresses the problem of vacant properties appearing cold and uninviting to potential buyers or renters, which can slow down sales and reduce perceived value. By uploading a photo of an empty room, users can select from various design styles—such as modern, rustic, or Scandinavian—and the AI automatically generates a realistically staged image. This process eliminates the need for expensive physical furniture rental and manual photo editing, saving time and money. The platform is positioned as an accessible, on-demand solution for creating compelling visual content that helps properties sell faster and at higher prices by allowing buyers to visualize the space as a livable home.

VideoNeXt is an advanced video understanding framework developed by Microsoft Research that focuses on efficient and effective spatiotemporal modeling for video analysis tasks. It represents a significant advancement in video recognition technology by introducing a novel approach to processing video data through decomposed spatial and temporal modeling. The framework is designed to address the computational challenges of video analysis while maintaining high accuracy across various video understanding benchmarks. VideoNeXt employs innovative architectural designs that separate spatial and temporal processing pathways, allowing for more efficient computation and better feature extraction. This research tool is primarily targeted at computer vision researchers, AI engineers, and developers working on video analysis applications such as action recognition, video classification, and temporal understanding. It solves the fundamental problem of how to effectively model both spatial appearance and temporal dynamics in videos without excessive computational overhead. The framework is positioned as a state-of-the-art solution for video understanding tasks, offering improved performance over previous methods while being more computationally efficient. VideoNeXt demonstrates Microsoft's commitment to advancing video AI research and provides the research community with a powerful tool for developing next-generation video understanding systems.

Video Swin Transformer is an open-source deep learning model architecture designed for video understanding tasks. Developed by researchers from Microsoft Research Asia and other institutions, it adapts the Swin Transformer, originally designed for images, to the video domain by introducing a hierarchical spatiotemporal shifted window attention mechanism. This approach efficiently models local and global dependencies across both space and time in video data. It is primarily used by AI researchers, computer vision engineers, and data scientists for tasks like action recognition, video classification, and temporal modeling. The model addresses the challenge of high computational cost in video analysis by providing a scalable and effective transformer-based solution. It is positioned as a state-of-the-art research framework, often serving as a benchmark in academic papers and competitions. Users typically implement it via its public GitHub repository, which provides code, pre-trained models, and instructions for training and inference on custom datasets.

The VGG Image Annotator (VIA) is a standalone, open-source software tool developed by the Visual Geometry Group (VGG) at the University of Oxford for manually annotating images, audio, and video. It is a lightweight, web-based application that runs directly in a browser without requiring any installation or setup, making it highly accessible. VIA is designed for creating ground-truth data for computer vision and machine learning projects, supporting a wide range of annotation tasks including object detection (bounding boxes), image segmentation (polygons, circles, ellipses), and classification. It is widely used by researchers, students, and practitioners in academia and industry for tasks like building datasets for object recognition, facial landmark detection, and medical image analysis. The tool saves annotations in a simple JSON file format, promoting easy integration with other data processing pipelines. Its focus is on simplicity, privacy (as data never leaves the user's computer), and flexibility for custom annotation schemas.

V7 is an AI-powered training data platform designed to automate the creation and management of annotated datasets for computer vision models. It serves machine learning teams, data scientists, and engineers working on visual AI projects across industries like healthcare, manufacturing, and autonomous vehicles. The platform combines a no-code annotation workspace with automated labeling models to significantly accelerate the data preparation pipeline. Users can upload images or video, leverage pre-trained or custom AI models to auto-annotate objects, and then collaboratively refine the labels. V7 addresses the critical bottleneck of high-quality training data by streamlining annotation, enabling active learning workflows, and providing dataset management tools. Its focus is on turning raw visual data into production-ready datasets efficiently, reducing manual effort from weeks to days.

Undetectable AI is a specialized tool designed to analyze and transform AI-generated text to make it appear more human-written. It serves two primary functions: detecting whether content was likely created by AI models like ChatGPT, GPT-4, Claude, or others, and then 'humanizing' that content by rewriting it to bypass AI detection systems. The platform is used by content creators, marketers, students, and professionals who need to ensure their AI-assisted writing passes scrutiny from plagiarism checkers, academic integrity systems, and content quality evaluators. It addresses the growing challenge of AI detection in educational, professional, and publishing contexts where authenticity is valued. The tool analyzes text against multiple detection algorithms simultaneously and provides rewriting suggestions that maintain the original meaning while altering patterns that trigger AI detection flags. Users can check content before submission and receive detailed reports on AI probability scores from various detectors.

The TIMIT Acoustic-Phonetic Continuous Speech Corpus is a foundational dataset for speech recognition research, developed by a consortium including Texas Instruments and MIT in the late 1980s. It contains high-quality recordings of 630 speakers from eight major dialect regions of the United States, each reading ten phonetically rich sentences. The corpus includes time-aligned orthographic, phonetic, and word transcriptions, making it invaluable for training and evaluating automatic speech recognition (ASR) systems and studying acoustic-phonetic phenomena. Researchers use TIMIT to benchmark phoneme recognition accuracy, develop speaker-independent models, and study dialectal variations in American English. Despite its age, TIMIT remains a standard reference dataset in speech technology research due to its careful design, comprehensive annotations, and widespread adoption in academic publications. The corpus is distributed by the Linguistic Data Consortium (LDC) under license and serves as a controlled environment for comparing different speech processing algorithms under consistent conditions.

TimeSformer is a state-of-the-art video understanding model developed by Facebook AI Research (FAIR) that introduces a novel 'divided space-time attention' mechanism. Unlike traditional 3D convolutional neural networks that process video data through computationally expensive 3D convolutions, TimeSformer applies self-attention separately across spatial and temporal dimensions. This architecture enables efficient processing of long video sequences while maintaining high accuracy on action recognition tasks. The model is designed for researchers and practitioners working on video analysis, requiring PyTorch and significant GPU resources for training and inference. It represents a shift from convolutional approaches to transformer-based architectures for video, offering better computational efficiency and scalability to longer videos. The open-source implementation includes pre-trained models on datasets like Kinetics-400, Something-Something-V2, and HowTo100M, making it accessible for academic and industrial applications in video classification, temporal localization, and action understanding.

This Person Does Not Exist is a pioneering web demonstration that generates highly realistic, synthetic human faces using artificial intelligence. The tool serves as a public showcase for StyleGAN, a generative adversarial network architecture developed by NVIDIA. Each time the website is refreshed, it produces a completely new, photorealistic portrait of a person who does not actually exist, created by the AI model trained on a vast dataset of real human faces. The primary purpose is educational and demonstrative, illustrating the capabilities and potential ethical implications of modern AI in creating synthetic media. It has become a widely recognized internet phenomenon, used by developers, researchers, educators, and the general public to understand generative AI. The site requires no registration or payment, offering instant access to AI-generated imagery that challenges perceptions of reality and authenticity in digital media.

Themis is an open-source AI tool developed by researchers at the University of Massachusetts Amherst's LASER Lab that detects and measures social bias in hiring and recruitment systems. It functions as a bias auditing framework specifically designed to evaluate AI-powered hiring platforms, resume screening tools, and automated recruitment systems. The tool simulates job applicants with different demographic attributes (gender, race, ethnicity) but identical qualifications to test whether AI hiring systems exhibit discriminatory patterns. Researchers, HR professionals, and AI ethics teams use Themis to audit existing hiring algorithms, benchmark fairness improvements, and ensure compliance with anti-discrimination regulations. Unlike generic fairness toolkits, Themis focuses specifically on the hiring domain with realistic job application scenarios and standardized bias metrics. The tool helps organizations identify unintended discrimination in automated hiring processes before they impact real candidates, supporting more equitable employment practices. It's particularly valuable for companies deploying AI in recruitment, regulatory bodies monitoring algorithmic fairness, and researchers studying bias in employment technologies.