Best AI tools for< Check Face Symmetry >

FaceSymAI is an online tool that utilizes advanced AI algorithms to analyze and determine the symmetry of your face. By uploading a photo, the AI examines your facial features, including the eyes, nose, mouth, and overall structure, to provide an accurate assessment of your facial symmetry. The analysis is based on mathematical and statistical methods, ensuring reliable and precise results. FaceSymAI is designed to be user-friendly and accessible, offering a free service to everyone. The uploaded photos are treated with utmost confidentiality and are not stored or used for any other purpose, ensuring your privacy is respected.

PimEyes is an online face search engine that uses face recognition technology to find pictures containing given faces. It is a great tool to audit copyright infringement, protect your privacy, and find people.

Recognito is a leading facial recognition technology provider, offering the NIST FRVT Top 1 Face Recognition Algorithm. Their high-performance biometric technology is used by police forces and security services to enhance public safety, manage individual movements, and improve audience analytics for businesses. Recognito's software goes beyond object detection to provide detailed user role descriptions and develop user flows. The application enables rapid face and body attribute recognition, video analytics, and artificial intelligence analysis. With a focus on security, living, and business improvements, Recognito helps create safer and more prosperous cities.

SocialFinder.ai is an AI-powered tool that enables users to find anyone online with a single photo. The tool uses cutting-edge AI technology to scan the web, locate social media profiles, determine image geolocation, and verify the identity of individuals. It helps users quickly and effectively find someone's digital footprint, making it a valuable resource for various fields, including investigations, background checks, and event planning.

Bibit AI is a real estate marketing AI designed to enhance the efficiency and effectiveness of real estate marketing and sales. It can help create listings, descriptions, and property content, and offers a host of other features. Bibit AI is the world's first AI for Real Estate. We are transforming the real estate industry by boosting efficiency and simplifying tasks like listing creation and content generation.

ZeroGPT is a comprehensive AI detection tool that helps users identify AI-generated content. It offers a range of features, including sentence highlighting, batch file upload, high accuracy, and support for multiple languages. ZeroGPT's DeepAnalyse™ Technology employs a multi-stage methodology to analyze text and determine its origin. The tool is designed to minimize false positives and negatives, providing users with reliable results. ZeroGPT also offers a user-friendly API for organizations, enabling them to integrate the tool into their systems.

GPTKit is a free AI text generation detection tool that utilizes six different AI-based content detection techniques to identify and classify text as either human- or AI-generated. It provides reports on the authenticity and reality of the analyzed content, with an accuracy of approximately 93%. The first 2048 characters in every request are free, and users can register for free to get 2048 characters/request.

ZeroGPT is a trusted AI detector tool that specializes in detecting AI-generated content like ChatGPT, GPT4, and Gemini. It offers advanced features such as AI summarization, paraphrasing, grammar and spell checking, translation, word counting, and citation generation. The tool is designed to provide highly accurate results and supports multiple languages. ZeroGPT stands out for its highlighted sentences feature, batch file upload capability, high accuracy model, and automatically generated reports. It utilizes DeepAnalyse™ Technology, a multi-stage methodology that optimizes accuracy while minimizing false positives and negatives. Users can unlock premium features and API access to enhance their writing skills and integrate the tool on a large scale.

AI Baby Generator is an AI application that allows users to predict the face of their future baby by analyzing their own features and those of their partner. The app uses advanced Artificial Intelligence technology to provide accurate results in just one tap. Users can upload photos of themselves and their partner to see which parent the baby resembles more. With a user-friendly interface, the app offers a fun and interactive way to visualize the potential appearance of a future child.

Attune Health Mobile App is an AI-enabled application that offers contactless measurement of vital signs using video-based technology. Users can easily track and monitor their blood pressure, oxygen saturation, HRV, stress levels, and Hemoglobin through a simple face scan. The app provides accurate real-time measurements, empowering individuals to take control of their health and wellness. It also offers gender-specific results, privacy protection, and family value by allowing biomarker measurements for the whole family. Attune Health is a comprehensive solution for individuals and corporations seeking to improve health outcomes and productivity.

Lovi is a comprehensive AI-driven skin care application that offers science-backed cosmetology services. It provides personalized skin health analysis, helps users set skincare goals, tracks changes with face scanning technology, and ensures cosmetic safety. With a focus on user health and well-being, Lovi offers expert skincare guidance, product recommendations, and answers to skincare-related questions. The application is designed to cater to individual skin needs, offering a unique approach to maintaining a healthy lifestyle through self-care.

The LLM Token Counter is a sophisticated tool designed to help users effectively manage token limits for various Language Models (LLMs) like GPT-3.5, GPT-4, Claude-3, Llama-3, and more. It utilizes Transformers.js, a JavaScript implementation of the Hugging Face Transformers library, to calculate token counts client-side. The tool ensures data privacy by not transmitting prompts to external servers.

Essay Check is a free AI-powered tool that helps students, teachers, content creators, SEO specialists, and legal experts refine their writing, detect plagiarism, and identify AI-generated content. With its user-friendly interface and advanced algorithms, Essay Check analyzes text to identify grammatical errors, spelling mistakes, instances of plagiarism, and the likelihood that content was written using AI. The tool provides detailed feedback and suggestions to help users improve their writing and ensure its originality and authenticity.

Check Typo is an AI-powered spell-checker tool designed to assist users in eliminating typos and grammatical errors from their writing. It seamlessly integrates within various websites, supports multiple languages, and preserves the original text's style and tone. Ideal for students, professionals, and writers, Check Typo enhances the writing experience with AI-driven precision, making it perfect for error-free emails, professional networking on platforms like LinkedIn, and enhancing social media posts across different platforms.

Copyright Check AI is a service that helps protect brands from legal disputes related to copyright violations on social media. The software automatically detects copyright infringements on social profiles, reducing the risk of costly legal action. It is used by Heads of Marketing and In-House Counsel at top brands to avoid lawsuits and potential damages. The service offers a done-for-you audit to highlight violations, deliver reports, and provide ongoing monitoring to ensure brand protection.

Double Check AI is an AI homework helper designed to assist college students in completing their assignments quickly and accurately. It offers instant answers, detailed explanations, and advanced recognition capabilities for solving complex problems. The tool is undetectable and plagiarism-free, making it a valuable resource for students looking to boost their grades and save time on homework.

Fact Check Anything (FCA) is a browser extension that allows users to fact-check information on the internet. It uses AI to verify statements and provide users with reliable sources. FCA is available for all browsers using the Chromium engine on Windows or MacOS. It is easy to use and can be used on any website. FCA is a valuable tool for anyone who wants to stay informed and fight against misinformation.

Rizz Check is a swipe game where users can befriend AI celebrities and ask them on dates. The game is built with Rizz, a library created by boredhead00.

Gaslighting Check is an AI-powered tool designed to help users identify and understand manipulation patterns, particularly gaslighting, in their conversations. The tool offers text and voice analysis capabilities to detect subtle manipulation tactics, providing users with actionable insights and recommendations. Gaslighting Check aims to empower individuals by recognizing and documenting manipulation, ultimately aiding in regaining self-confidence and setting boundaries in various relationships.

LLM Price Check is an AI tool designed to compare and calculate the latest prices for Large Language Models (LLM) APIs from leading providers such as OpenAI, Anthropic, Google, and more. Users can use the streamlined tool to optimize their AI budget efficiently by comparing pricing, sorting by various parameters, and searching for specific models. The tool provides a comprehensive overview of pricing information to help users make informed decisions when selecting an LLM API provider.

AutoRound is an advanced weight-only quantization algorithm for low-bits LLM inference. It competes impressively against recent methods without introducing any additional inference overhead. The method adopts sign gradient descent to fine-tune rounding values and minmax values of weights in just 200 steps, often significantly outperforming SignRound with the cost of more tuning time for quantization. AutoRound is tailored for a wide range of models and consistently delivers noticeable improvements.

This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

Me LLaMA introduces a suite of open-source medical Large Language Models (LLMs), including Me LLaMA 13B/70B and their chat-enhanced versions. Developed through innovative continual pre-training and instruction tuning, these models leverage a vast medical corpus comprising PubMed papers, medical guidelines, and general domain data. Me LLaMA sets new benchmarks on medical reasoning tasks, making it a significant asset for medical NLP applications and research. The models are intended for computational linguistics and medical research, not for clinical decision-making without validation and regulatory approval.

The MiniAiLive Face Liveness Detection Android SDK provides advanced computer vision techniques to enhance security and accuracy on Android platforms. It offers 3D Passive Face Liveness Detection capabilities, ensuring that users are physically present and not using spoofing methods to access applications or services. The SDK is fully on-premise, with all processing happening on the hosting server, ensuring data privacy and security.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

AI-powered 3D Face Detection & Rotation Tracking, Face Description & Recognition, Body Pose Tracking, 3D Hand & Finger Tracking, Iris Analysis, Age & Gender & Emotion Prediction, Gaze Tracking, Gesture Recognition, Body Segmentation

Extrapolate is an app that uses Artificial Intelligence to show you how your face ages over time. It generates a 3-second GIF of your aging face and allows you to store and retrieve photos from Cloudflare R2 using Workers. Users can deploy their own version of Extrapolate on Vercel by setting up ReplicateHQ and Upstash accounts, as well as creating a Cloudflare R2 instance with a Cloudflare Worker to handle uploads and reads. The tool provides a fun and interactive way to visualize the aging process through AI technology.

Hezar is an all-in-one AI library designed specifically for the Persian community. It brings together various AI models and tools, making it easy to use AI with just a few lines of code. The library seamlessly integrates with Hugging Face Hub, offering a developer-friendly interface and task-based model interface. In addition to models, Hezar provides tools like word embeddings, tokenizers, feature extractors, and more. It also includes supplementary ML tools for deployment, benchmarking, and optimization.

Home-Gallery.org is a self-hosted open-source web gallery for browsing personal photos and videos with tagging, mobile-friendly interface, and AI-powered image and face discovery. It aims to provide a fast user experience on mobile phones and help users browse and rediscover memories from their media archive. The tool allows users to serve their local data without relying on cloud services, view photos and videos from mobile phones, and manage images from multiple media source directories. Features include endless photo stream, video transcoding, reverse image lookup, face detection, GEO location reverse lookups, tagging, and more. The tool runs on NodeJS and supports various platforms like Linux, Mac, and Windows.

Aphrodite is the official backend engine for PygmalionAI, serving as the inference endpoint for the website. It allows serving Hugging Face-compatible models with fast speeds. Features include continuous batching, efficient K/V management, optimized CUDA kernels, quantization support, distributed inference, and 8-bit KV Cache. The engine requires Linux OS and Python 3.8 to 3.12, with CUDA >= 11 for build requirements. It supports various GPUs, CPUs, TPUs, and Inferentia. Users can limit GPU memory utilization and access full commands via CLI.

LLMPerf is a tool designed for evaluating the performance of Language Model APIs. It provides functionalities for conducting load tests to measure inter-token latency and generation throughput, as well as correctness tests to verify the responses. The tool supports various LLM APIs including OpenAI, Anthropic, TogetherAI, Hugging Face, LiteLLM, Vertex AI, and SageMaker. Users can set different parameters for the tests and analyze the results to assess the performance of the LLM APIs. LLMPerf aims to standardize prompts across different APIs and provide consistent evaluation metrics for comparison.

AI Toolkit for Visual Studio Code simplifies generative AI app development by bringing together cutting-edge AI development tools and models from Azure AI Studio Catalog and other catalogs like Hugging Face. Users can browse the AI models catalog, download them locally, fine-tune, test, and deploy them to the cloud. The toolkit offers actions such as finding supported models, testing model inference, fine-tuning models locally or remotely, and deploying fine-tuned models to the cloud. It also provides optimized AI models for Windows and a Q&A section for common issues and resolutions.

Discord Midjourney Bot is an open-source bot designed for AI enthusiasts, providing various AI art functionalities without any paywalls. Users can enjoy features like text to image conversion, image transformation, logo generation, face swap, image upscaling, and more. The bot aims to offer advanced customizable image generation capabilities, including access to language models and canvas size customization. Additionally, the project is open to partnerships and investments, with opportunities for bloggers to review the product. The bot requires Node v18+ to run and integrates with Replicate API for certain functionalities.

KsanaLLM is a high-performance engine for LLM inference and serving. It utilizes optimized CUDA kernels for high performance, efficient memory management, and detailed optimization for dynamic batching. The tool offers flexibility with seamless integration with popular Hugging Face models, support for multiple weight formats, and high-throughput serving with various decoding algorithms. It enables multi-GPU tensor parallelism, streaming outputs, and an OpenAI-compatible API server. KsanaLLM supports NVIDIA GPUs and Huawei Ascend NPU, and seamlessly integrates with verified Hugging Face models like LLaMA, Baichuan, and Qwen. Users can create a docker container, clone the source code, compile for Nvidia or Huawei Ascend NPU, run the tool, and distribute it as a wheel package. Optional features include a model weight map JSON file for models with different weight names.

WildBench is a tool designed for benchmarking Large Language Models (LLMs) with challenging tasks sourced from real users in the wild. It provides a platform for evaluating the performance of various models on a range of tasks. Users can easily add new models to the benchmark by following the provided guidelines. The tool supports models from Hugging Face and other APIs, allowing for comprehensive evaluation and comparison. WildBench facilitates running inference and evaluation scripts, enabling users to contribute to the benchmark and collaborate on improving model performance.

Recognize is a smart media tagging tool for Nextcloud that automatically categorizes photos and music by recognizing faces, animals, landscapes, food, vehicles, buildings, landmarks, monuments, music genres, and human actions in videos. It uses pre-trained models for object detection, landmark recognition, face comparison, music genre classification, and video classification. The tool ensures privacy by processing images locally without sending data to cloud providers. However, it cannot process end-to-end encrypted files. Recognize is rated positively for ethical AI practices in terms of open-source software, freely available models, and training data transparency, except for music genre recognition due to limited access to training data.

Liger Kernel is a collection of Triton kernels designed for LLM training, increasing training throughput by 20% and reducing memory usage by 60%. It includes Hugging Face Compatible modules like RMSNorm, RoPE, SwiGLU, CrossEntropy, and FusedLinearCrossEntropy. The tool works with Flash Attention, PyTorch FSDP, and Microsoft DeepSpeed, aiming to enhance model efficiency and performance for researchers, ML practitioners, and curious novices.

Guides on checking and monitoring credit scores, with a financial and informative tone.

Drop in any requirements ; I'll reduces ambiguity using requirement health check

Calculate website worth by analyzing monthly revenue, using industry-standard valuation methods to provide approximate, informative value estimates.

Balances out bias and researches live reports to give you a more balanced view (Paste in the text you want to check)

Check the weirdness of a thing.

GPT expert on CISA's KEV Catalog and CVE existence check

Check your coding skill from uploaded codes.

I'm an English teacher here to check and simplify your messages.

I check code samples for common mistakes and accuracy

Helps check and provide feedback on service providers like contractors and plumbers.

A mental health self-check tool assessing weather dependency. Powered by WeatherMind

I'll help you sense-check and define Behavioral Insights for your ideas!

Helps find food deals and check for fair food pricing.

Enhanced FAANG Writing coach with spell check and readability tools

ChatGPT Essay Writer helps you to write essays with OpenAI. Generate Professional Essays with Plagiarism Check, Formatting, Cost Estimation & More.

Provides in-depth insights based on familiarity with the historical & cultural context of biblical times including an understanding of theological concepts. It's a Bible Scholar in your pocket!!! Verify Before You Trust (VBYT): Always Double-Check ChatGPT's Insights!

Calculate the results of your A/B test and check whether the result is statistically significant or due to chance.

EXPERIMENT - Friendly guide for navigating Anchorage Municipal Code - Double Check info

Expert in crafting personalized low FODMAP recipes with representative images. Cross check all ingredients & low FODMAP servings.

Ai PDF is a GPT (uses the popular Ai PDF plugin) that allows you to chat and ask questions of your PDF documents and have it explained to you by ChatGPT. We also include page references to help you fact-check all answers.