Best AI tools for< Identify Objects >

Luxi is an AI-powered tool that enables users to automatically discover items in images. By leveraging advanced image recognition technology, Luxi can accurately identify objects within images, making it easier for users to search, categorize, and analyze visual content. With Luxi, users can streamline their image processing workflows, saving time and effort in identifying and tagging objects within large image datasets.

Photor AI is an AI tool designed for analyzing and selecting user's best photos. With over 1,000,000 photos already analyzed, it offers a smart way to evaluate your photos. In addition to photo analysis, the tool provides resources on taking professional headshots with a smartphone, choosing the best colors to wear for pictures, and offers a free photo editor. The website also includes customer portal, blog, and information about the company.

Google Lens is an AI-powered visual search tool developed by Google that allows users to search, shop, translate, and identify objects using their camera or images. With Google Lens, users can find similar clothes, furniture, and home decor, translate text in real-time from over 100 languages, get step-by-step homework help for various subjects, and identify plants and animals. The application is available on all devices and in various Google apps, making it convenient for users to access its features anytime, anywhere.

iNCSAI List is a comprehensive database of AI startups and companies. It provides information on the latest AI trends, news, and resources. The website also offers a directory of AI companies, sorted by industry and location. iNCSAI List is a valuable resource for anyone interested in learning more about AI or finding AI-related products and services.

Image Bear AI is an advanced image recognition tool that utilizes artificial intelligence to analyze and identify objects within images. The application is designed to assist users in various industries such as e-commerce, security, and healthcare by providing accurate and efficient image analysis capabilities. With its cutting-edge technology, Image Bear AI offers a user-friendly interface and fast processing speeds, making it a valuable tool for businesses looking to streamline their image recognition processes.

Be My Eyes is a free mobile app that connects blind and low-vision people with sighted volunteers and AI-powered assistance. With Be My Eyes, blind and low-vision people can access visual information, get help with everyday tasks, and connect with others in the community. Be My Eyes is available in over 180 languages and has over 6 million volunteers worldwide.

Vidrovr is a video analysis platform that uses machine learning to process unstructured video, image, or audio data. It provides business insights to help drive revenue, make strategic decisions, and automate monotonous processes within a business. Vidrovr's technology can be used to minimize equipment downtime, proactively plan for equipment replacement, leverage AI to empower mission objectives and decision making, monitor persons or topics of interest across various media sources, ensure critical infrastructure is monitored 24/7/365, and protect ecological assets.

Meta AI is an advanced artificial intelligence tool that enables users to learn, create, and explore the world around them. With features like AI Studio for creating custom AIs and Llama for building the future of AI, Meta AI offers cutting-edge technology to bring visions to life. Users can engage with AI characters, identify objects, and have conversations using voice commands. The platform is designed to make AI more accessible and engaging for everyone, with a focus on open collaboration and innovation.

Stork is an AI App Directory & Marketplace that provides a comprehensive listing of over 9000 AI tools and agents. The platform allows users to search and discover AI tools based on their specific needs and preferences. Stork also offers a variety of resources and support to help users get the most out of AI technology.

Peqaboo is an AI-powered pet social app designed to help pet owners with various aspects of pet care. The app allows users to ask Boo AI questions about their pets, identify toxic plants or foods, and receive instant answers based on their pet's profile. Peqaboo also offers a feature to train a new Boo AI, enabling users to transform their knowledge into AI tools. The app aims to make pet life easier and more enjoyable by providing personalized pet care advice and fostering a global pet community.

Rank One Computing (ROC) is an American-made provider of multimodal biometrics and computer vision solutions, specializing in face recognition, fingerprint recognition, and artificial intelligence technologies. Trusted by the U.S. military, law enforcement, and leading FinTech brands, ROC offers top-ranked software for identity proofing and threat detection. Their suite of products includes ROC SDK, ROC Watch, and custom enterprise AI development services. With a focus on security and efficiency, ROC aims to make the world safer and more convenient through unbiased and privacy-conscious applications.

Greyparrot provides AI-powered waste analytics solutions for recycling facilities and packaging companies. Their AI waste analytics platform, Greyparrot Analyzer, uses cameras to track materials passing on conveyor belts and translates images into real-time insights on a live dashboard. Greyparrot Sync connects that live data stream to existing or new hardware and software. Greyparrot's AI identifies all of the waste objects found in global municipal recovery sites, with 67 waste categories and counting. Their AI waste analytics enable automation in sorting facilities and increase transparency at each stage of the global value chain.

Venture Planner is an AI-powered platform designed to help users generate professional business plans effortlessly. By answering a series of multiple-choice questions, users can create detailed financial forecasts without the need for typing. The platform is fully bespoke to each user's business, offering automated projections, professional quality plans, and strategy suggestions. With over 50,000 users across 74 industries in 22 countries, Venture Planner leverages cutting-edge AI technology to outpace competitors and provide data-driven insights for informed decision-making.

mapEDU is an AI-powered curriculum mapping and exam tagging software designed specifically for healthcare professions schools. It uses natural language processing and machine learning to automatically extract relevant MeSH tags from existing digital content, map events/courses/programs with outcomes, and auto-tag exam questions. This provides healthcare professions schools with objective, actionable data to improve curriculum design, validate revisions, and enhance student performance analytics.

ScrumDesk is an online scrum and kanban project management tool for agile teams. It supports objectives and key results, user stories mapping, retrospectives, root cause analysis and many great agile practices. Since 2007.

Ergodic is an AI tool called Kepler that empowers businesses to make data-driven decisions. Kepler acts as an AI action engine, bridging the knowledge gap between business context and data insights. It goes beyond number crunching to help businesses build scenarios, evaluate outcomes, and take action based on objectives. With a focus on action-first approach, Kepler streamlines decision-making processes by providing actionable insights for optimizing processes, identifying opportunities, and mitigating risks.

Career Copilot is an AI-powered hiring tool that helps recruiters and hiring managers find the best candidates for their open positions. The tool uses machine learning to analyze candidate profiles and identify those who are most qualified for the job. Career Copilot also provides a number of features to help recruiters streamline the hiring process, such as candidate screening, interview scheduling, and offer management.

The Frontier Model Forum (FMF) is a collaborative effort among leading AI companies to advance AI safety and responsibility. The FMF brings together technical and operational expertise to identify best practices, conduct research, and support the development of AI applications that meet society's most pressing needs. The FMF's core objectives include advancing AI safety research, identifying best practices, collaborating across sectors, and helping AI meet society's greatest challenges.

Bestlook is an AI-powered application that helps individuals and professionals in the beauty industry to enhance their appearance through objective and personalized recommendations. By leveraging Artificial Intelligence, Bestlook analyzes beauty insights from millions of people to identify the most captivating facial features. Users can preview before and after simulations with a simple click, ensuring a confident cosmetic journey. The application offers tailored plans for individuals and professionals, providing unlimited manual simulations and AI predictions. Bestlook aims to redefine beauty standards by offering universally admired looks.

Resume Screening AI is an AI application designed to help recruiters, hiring managers, and HR managers screen resumes in bulk efficiently and accurately. By leveraging AI algorithms, the tool automates the screening process, saving time and improving the quality of hire. It offers benefits such as time and cost savings, improved accuracy, enhanced objectivity, and a better candidate experience. The tool uses end-to-end encryption for data security and stores resume file fingerprints and parsed text for easy retrieval. With a focus on optimizing the recruitment process, Resume Screening AI is a transformative solution for businesses looking to attract and identify the most suitable candidates.

OpenGlass is an open-source project that allows users to transform any regular glasses into smart glasses using affordable off-the-shelf components. With a cost of less than $25, users can enhance their glasses to record their daily activities, recognize people, identify objects, translate text, and more. The project provides detailed instructions on hardware setup and software installation, making it accessible for DIY enthusiasts and tech enthusiasts alike. By following the steps outlined in the repository, users can create their own smart glasses and explore various functionalities offered by the project.

The Deep Java Library (DJL) is a framework-agnostic Java API for deep learning. It provides a unified interface to popular deep learning frameworks such as TensorFlow, PyTorch, and MXNet. DJL makes it easy to develop deep learning applications in Java, and it can be used for a variety of tasks, including image classification, object detection, natural language processing, and speech recognition.

The LandingLens Python library contains the LandingLens development library and examples that show how to integrate your app with LandingLens in a variety of scenarios. The library allows users to acquire images from different sources, run inference on computer vision models deployed in LandingLens, and provides examples in Jupyter Notebooks and Python apps for various tasks such as object detection, home automation, satellite image analysis, license plate detection, and streaming video analysis.

BIG-Bench Mistake is a dataset of chain-of-thought (CoT) outputs annotated with the location of the first logical mistake. It was released as part of a research paper focusing on benchmarking LLMs in terms of their mistake-finding ability. The dataset includes CoT traces for tasks like Word Sorting, Tracking Shuffled Objects, Logical Deduction, Multistep Arithmetic, and Dyck Languages. Human annotators were recruited to identify mistake steps in these tasks, with automated annotation for Dyck Languages. Each JSONL file contains input questions, steps in the chain of thoughts, model's answer, correct answer, and the index of the first logical mistake.

This Helm chart bootstraps an Airflow deployment on a Kubernetes cluster using the Helm package manager. The version of this chart does not correlate to any other component. Users should not expect feature parity between OSS airflow chart and the Astronomer airflow-chart for identical version numbers. To install this helm chart remotely (using helm 3) kubectl create namespace airflow helm repo add astronomer https://helm.astronomer.io helm install airflow --namespace airflow astronomer/airflow To install this repository from source sh kubectl create namespace airflow helm install --namespace airflow . Prerequisites: Kubernetes 1.12+ Helm 3.6+ PV provisioner support in the underlying infrastructure Installing the Chart: sh helm install --name my-release . The command deploys Airflow on the Kubernetes cluster in the default configuration. The Parameters section lists the parameters that can be configured during installation. Upgrading the Chart: First, look at the updating documentation to identify any backwards-incompatible changes. To upgrade the chart with the release name `my-release`: sh helm upgrade --name my-release . Uninstalling the Chart: To uninstall/delete the `my-release` deployment: sh helm delete my-release The command removes all the Kubernetes components associated with the chart and deletes the release. Updating DAGs: Bake DAGs in Docker image The recommended way to update your DAGs with this chart is to build a new docker image with the latest code (`docker build -t my-company/airflow:8a0da78 .`), push it to an accessible registry (`docker push my-company/airflow:8a0da78`), then update the Airflow pods with that image: sh helm upgrade my-release . --set images.airflow.repository=my-company/airflow --set images.airflow.tag=8a0da78 Docker Images: The Airflow image that are referenced as the default values in this chart are generated from this repository: https://github.com/astronomer/ap-airflow. Other non-airflow images used in this chart are generated from this repository: https://github.com/astronomer/ap-vendor. Parameters: The complete list of parameters supported by the community chart can be found on the Parameteres Reference page, and can be set under the `airflow` key in this chart. The following tables lists the configurable parameters of the Astronomer chart and their default values. | Parameter | Description | Default | | :----------------------------- | :-------------------------------------------------------------------------------------------------------- | :---------------------------- | | `ingress.enabled` | Enable Kubernetes Ingress support | `false` | | `ingress.acme` | Add acme annotations to Ingress object | `false` | | `ingress.tlsSecretName` | Name of secret that contains a TLS secret | `~` | | `ingress.webserverAnnotations` | Annotations added to Webserver Ingress object | `{}` | | `ingress.flowerAnnotations` | Annotations added to Flower Ingress object | `{}` | | `ingress.baseDomain` | Base domain for VHOSTs | `~` | | `ingress.auth.enabled` | Enable auth with Astronomer Platform | `true` | | `extraObjects` | Extra K8s Objects to deploy (these are passed through `tpl`). More about Extra Objects. | `[]` | | `sccEnabled` | Enable security context constraints required for OpenShift | `false` | | `authSidecar.enabled` | Enable authSidecar | `false` | | `authSidecar.repository` | The image for the auth sidecar proxy | `nginxinc/nginx-unprivileged` | | `authSidecar.tag` | The image tag for the auth sidecar proxy | `stable` | | `authSidecar.pullPolicy` | The K8s pullPolicy for the the auth sidecar proxy image | `IfNotPresent` | | `authSidecar.port` | The port the auth sidecar exposes | `8084` | | `gitSyncRelay.enabled` | Enables git sync relay feature. | `False` | | `gitSyncRelay.repo.url` | Upstream URL to the git repo to clone. | `~` | | `gitSyncRelay.repo.branch` | Branch of the upstream git repo to checkout. | `main` | | `gitSyncRelay.repo.depth` | How many revisions to check out. Leave as default `1` except in dev where history is needed. | `1` | | `gitSyncRelay.repo.wait` | Seconds to wait before pulling from the upstream remote. | `60` | | `gitSyncRelay.repo.subPath` | Path to the dags directory within the git repository. | `~` | Specify each parameter using the `--set key=value[,key=value]` argument to `helm install`. For example, sh helm install --name my-release --set executor=CeleryExecutor --set enablePodLaunching=false . Walkthrough using kind: Install kind, and create a cluster We recommend testing with Kubernetes 1.25+, example: sh kind create cluster --image kindest/node:v1.25.11 Confirm it's up: sh kubectl cluster-info --context kind-kind Add Astronomer's Helm repo sh helm repo add astronomer https://helm.astronomer.io helm repo update Create namespace + install the chart sh kubectl create namespace airflow helm install airflow -n airflow astronomer/airflow It may take a few minutes. Confirm the pods are up: sh kubectl get pods --all-namespaces helm list -n airflow Run `kubectl port-forward svc/airflow-webserver 8080:8080 -n airflow` to port-forward the Airflow UI to http://localhost:8080/ to confirm Airflow is working. Login as _admin_ and password _admin_. Build a Docker image from your DAGs: 1. Start a project using astro-cli, which will generate a Dockerfile, and load your DAGs in. You can test locally before pushing to kind with `astro airflow start`. `sh mkdir my-airflow-project && cd my-airflow-project astro dev init` 2. Then build the image: `sh docker build -t my-dags:0.0.1 .` 3. Load the image into kind: `sh kind load docker-image my-dags:0.0.1` 4. Upgrade Helm deployment: sh helm upgrade airflow -n airflow --set images.airflow.repository=my-dags --set images.airflow.tag=0.0.1 astronomer/airflow Extra Objects: This chart can deploy extra Kubernetes objects (assuming the role used by Helm can manage them). For Astronomer Cloud and Enterprise, the role permissions can be found in the Commander role. yaml extraObjects: - apiVersion: batch/v1beta1 kind: CronJob metadata: name: "{{ .Release.Name }}-somejob" spec: schedule: "*/10 * * * *" concurrencyPolicy: Forbid jobTemplate: spec: template: spec: containers: - name: myjob image: ubuntu command: - echo args: - hello restartPolicy: OnFailure Contributing: Check out our contributing guide! License: Apache 2.0 with Commons Clause

Simple-OpenAI is a Java library that provides a simple way to interact with the OpenAI API. It offers consistent interfaces for various OpenAI services like Audio, Chat Completion, Image Generation, and more. The library uses CleverClient for HTTP communication, Jackson for JSON parsing, and Lombok to reduce boilerplate code. It supports asynchronous requests and provides methods for synchronous calls as well. Users can easily create objects to communicate with the OpenAI API and perform tasks like text-to-speech, transcription, image generation, and chat completions.

Invariant Analyzer is an open-source scanner designed for LLM-based AI agents to find bugs, vulnerabilities, and security threats. It scans agent execution traces to identify issues like looping behavior, data leaks, prompt injections, and unsafe code execution. The tool offers a library of built-in checkers, an expressive policy language, data flow analysis, real-time monitoring, and extensible architecture for custom checkers. It helps developers debug AI agents, scan for security violations, and prevent security issues and data breaches during runtime. The analyzer leverages deep contextual understanding and a purpose-built rule matching engine for security policy enforcement.

Functionary is a language model that interprets and executes functions/plugins. It determines when to execute functions, whether in parallel or serially, and understands their outputs. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls. It offers documentation and examples on functionary.meetkai.com. The newest model, meetkai/functionary-medium-v3.1, is ranked 2nd in the Berkeley Function-Calling Leaderboard. Functionary supports models with different context lengths and capabilities for function calling and code interpretation. It also provides grammar sampling for accurate function and parameter names. Users can deploy Functionary models serverlessly using Modal.com.

The collection of pre-trained, state-of-the-art AI models. ailia SDK is a self-contained, cross-platform, high-speed inference SDK for AI. The ailia SDK provides a consistent C++ API across Windows, Mac, Linux, iOS, Android, Jetson, and Raspberry Pi platforms. It also supports Unity (C#), Python, Rust, Flutter(Dart) and JNI for efficient AI implementation. The ailia SDK makes extensive use of the GPU through Vulkan and Metal to enable accelerated computing. # Supported models 323 models as of April 8th, 2024

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

MME-RealWorld is a benchmark designed to address real-world applications with practical relevance, featuring 13,366 high-resolution images and 29,429 annotations across 43 tasks. It aims to provide substantial recognition challenges and overcome common barriers in existing Multimodal Large Language Model benchmarks, such as small data scale, restricted data quality, and insufficient task difficulty. The dataset offers advantages in data scale, data quality, task difficulty, and real-world utility compared to existing benchmarks. It also includes a Chinese version with additional images and QA pairs focused on Chinese scenarios.

Awesome-Segment-Anything is a powerful tool for segmenting and extracting information from various types of data. It provides a user-friendly interface to easily define segmentation rules and apply them to text, images, and other data formats. The tool supports both supervised and unsupervised segmentation methods, allowing users to customize the segmentation process based on their specific needs. With its versatile functionality and intuitive design, Awesome-Segment-Anything is ideal for data analysts, researchers, content creators, and anyone looking to efficiently extract valuable insights from complex datasets.

Zshot is a highly customizable framework for performing Zero and Few shot named entity and relationships recognition. It can be used for mentions extraction, wikification, zero and few shot named entity recognition, zero and few shot named relationship recognition, and visualization of zero-shot NER and RE extraction. The framework consists of two main components: the mentions extractor and the linker. There are multiple mentions extractors and linkers available, each serving a specific purpose. Zshot also includes a relations extractor and a knowledge extractor for extracting relations among entities and performing entity classification. The tool requires Python 3.6+ and dependencies like spacy, torch, transformers, evaluate, and datasets for evaluation over datasets like OntoNotes. Optional dependencies include flair and blink for additional functionalities. Zshot provides examples, tutorials, and evaluation methods to assess the performance of the components.

aiocsv is a Python module that provides asynchronous CSV reading and writing. It is designed to be a drop-in replacement for the Python's builtin csv module, but with the added benefit of being able to read and write CSV files asynchronously. This makes it ideal for use in applications that need to process large CSV files efficiently.

Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow LLM generations to make tool-use decisions, we use the magic of semantic vector space to make those decisions — _routing_ our requests using _semantic_ meaning.

DL3DV-10K is a large-scale dataset of real-world scene-level videos with annotations, covering diverse scenes with different levels of reflection, transparency, and lighting. It includes 10,510 multi-view scenes with 51.2 million frames at 4k resolution, and offers benchmark videos for novel view synthesis (NVS) methods. The dataset is designed to facilitate research in deep learning-based 3D vision and provides valuable insights for future research in NVS and 3D representation learning.

MaxText is a high-performance, highly scalable, open-source LLM written in pure Python/Jax and targeting Google Cloud TPUs and GPUs for training and inference. MaxText achieves high MFUs and scales from single host to very large clusters while staying simple and "optimization-free" thanks to the power of Jax and the XLA compiler. MaxText aims to be a launching off point for ambitious LLM projects both in research and production. We encourage users to start by experimenting with MaxText out of the box and then fork and modify MaxText to meet their needs.

A chat interface using open source models, eg OpenAssistant or Llama. It is a SvelteKit app and it powers the HuggingChat app on hf.co/chat.

Identifies and explains pointed-at objects in photos.

Wondering what something might be worth? Get started instantly - just upload an image!

Image & document analysis expert

All-encompassing antique and collectible appraisal assistant offering dollar estimates.

Assists visually impaired with image description and navigation.

Generates GUIDs with technical precision

Casual and friendly AI guide for easy learning

Specializes in discussing valuable items.

Expert in space exploration, astronomy, and celestial events

Expert in detailed object descriptions from images

AI detective in a Victorian London metaverse, guiding through AI-generated mysteries.

Identify and cherish your vintage

AI OKR Coach is a tool designed to assist users in the process of creating and assessing OKR (Objectives and Key Results). It provides a structured and flexible approach to OKR setting and evaluation.

IQ Test is designed to simulate an IQ testing environment. It provides a formal and objective experience, delivering questions and processing answers in a straightforward manner.

AI Meeting Analyst: Summarizes transcripts, extracts key points and action items, conducts sentiment analysis. Offers advice and insights on meeting content, objectives, and outcomes for improved effectiveness.

Provide a Topic and an Audience and we'll help you create 1. Course description 2. Outline 3. Learning Outcomes 5. Skills-Knowledge-Attitude objectives 5. Key points per lesson

Neutralizes emotional language; shows changes

A virtual coach guiding users in discovering their IKIGAI through self-reflection and balanced insights.

Identifies hotdogs in images.

Critically analyze SLIs/SLOs with a deeper, provocative approach