Best AI tools for< Tag Objects >
20 - AI tool Sites
Luxi
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.
Custom Vision
Custom Vision is a cognitive service provided by Microsoft that offers a user-friendly platform for creating custom computer vision models. Users can easily train the models by providing labeled images, allowing them to tailor the models to their specific needs. The service simplifies the process of implementing visual intelligence into applications, making it accessible even to those without extensive machine learning expertise.
OpenTrain AI
OpenTrain AI is a data labeling marketplace that leverages artificial intelligence to streamline the process of labeling data for machine learning models. It provides a platform where users can crowdsource data labeling tasks to a global community of annotators, ensuring high-quality labeled datasets for training AI algorithms. With advanced AI algorithms and human-in-the-loop validation, OpenTrain AI offers efficient and accurate data labeling services for various industries such as autonomous vehicles, healthcare, and natural language processing.
mapEDU
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.
Ximilar Visual AI for Business
Ximilar Visual AI for Business is an AI tool that offers a comprehensive platform for image recognition and visual search solutions. It provides features such as image classification, regression, object detection, AI model combination, image annotation, and more. Users can easily build custom machine learning models without coding, access ready-to-use visual AI demos, and benefit from features like image upscaling, background removal, and color extraction. The platform caters to various industries including fashion, home decor, stock photos, collectibles, med & biotech, manufacturing, and real estate.
AI Tag Generator
AI Tag Generator is a free and powerful tool designed to help users generate optimized tags for their YouTube and Instagram content. It utilizes the latest AI technology to quickly identify content topics and generate relevant tags, enhancing content visibility and reach. The tool offers smart tag generation, large model technology for accuracy, user-friendly interface, real-time optimization, and multilingual support. With different pricing tiers, users can access various features like tag records, unlimited generations, and intelligent tag tracking. The tool is suitable for beginners, standard users, and professional users looking to improve their tagging system.
TagifyNow
TagifyNow is a free AI YouTube video tag generator and hashtag generator tool designed to simplify the process of selecting the perfect keywords for YouTube videos. It helps content creators reach a wider audience, save time, and boost visibility by generating SEO-friendly tags effortlessly. The tool offers features like brainstorming relevant keywords, trendspotting, competition analysis, and time-saving capabilities. TagifyNow ensures that users choose tags wisely to enhance their video's discoverability and avoid penalties from YouTube.
EtsyGenerator
EtsyGenerator is an AI-powered tool designed to assist Etsy sellers in creating high-quality product listings effortlessly. It offers a range of features such as generating product descriptions, titles, tags, and SEO content using intelligent machine learning models. The tool helps sellers save time and effort by automating the listing creation process, ultimately improving Etsy search rankings and attracting more potential customers. With a user-friendly interface, EtsyGenerator is a game-changer for beginners and experienced sellers alike, providing valuable ideas and simplifying the listing process.
Nero Platinum Suite
Nero Platinum Suite is a comprehensive software collection for Windows PCs that provides a wide range of multimedia capabilities, including burning, managing, optimizing, and editing photos, videos, and music files. It includes various AI-powered features such as the Nero AI Image Upscaler, Nero AI Video Upscaler, and Nero AI Photo Tagger, which enhance and simplify multimedia tasks.
Lang.ai
Lang.ai is an AI-powered customer experience (CX) insights and automation platform designed for mid-market businesses. It helps businesses unlock CX data, increase automation beyond chatbots, drive decisions based on relevant and accurate CX insights, and improve the overall customer experience. Lang.ai offers a range of features, including intelligent triage of complex requests, email automation, continuous improvement of chatbots, granular tagging, proactive alerts, automated discovery of new topics, and custom taxonomies. It integrates seamlessly with popular helpdesks such as Zendesk, Salesforce, Intercom, Kustomer, Dixa, and Freshworks.
AltTextGenerate
AltTextGenerate is a free online tool for generating alt text for images, which can boost your images' SEO in SERP. The tool uses AI-powered descriptions to provide suitable alt text for images, enhancing user experience and accessibility of websites. AltTextGenerate offers a comprehensive solution for generating alt text across various platforms, including WordPress, Shopify, and CMSs. It utilizes Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to understand image content and context, providing descriptive text for images.
Bibit AI
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.
PhotoTag.ai
PhotoTag.ai is an AI-powered platform that helps users generate tags, titles, and descriptions for photos and videos using cutting-edge AI technology. It enables users to save time by automating the keyword generation process, making it ideal for stock photography, e-commerce, marketing, and more. With features like customizable upload settings, batch processing, and multilingual support, PhotoTag.ai offers a seamless experience for content creators looking to enhance their workflow.
MLflow
MLflow is an open source platform for managing the end-to-end machine learning (ML) lifecycle, including tracking experiments, packaging models, deploying models, and managing model registries. It provides a unified platform for both traditional ML and generative AI applications.
ChatGPT
ChatGPT is a leading Chinese learning website that offers a comprehensive AI learning experience. It provides tutorials on ChatGPT, GPTs, and AI applications, guiding users from basic principles to advanced usage. The platform also offers ChatGPT Prompt words for various professions and life scenarios, inspiring creativity and productivity. Additionally, MidJourney tutorials focus on AI drawing, particularly suitable for beginners. With AI tools like AI Reading Assistant and GPT Finder, ChatGPT aims to enhance learning, work efficiency, and business success.
Playbook
Playbook is an AI-powered file manager for creatives, by creatives. It is the world's first collaborative creative space that combines the features of Dropbox and Pinterest, with 4TB of starter space. Playbook helps users organize, share, and collaborate on creative files and projects with their clients and team. It uses AI to organize work in a way that makes sense, and allows users to find files 10x faster than traditional cloud storage. Playbook also has a beautiful gallery feature that makes it easy to share work with clients and gather feedback.
PromptPanda
PromptPanda is an AI Prompt Management System designed to streamline workflow by securely managing prompts. It centralizes company prompts, allowing for efficient retrieval and comparison of new prompts. Users can explore and optimize market-tested prompts, ensuring consistent high-quality outcomes. The tool offers a central prompt repository for easy organization and clarity in AI usage.
Poly
Poly is a next-generation intelligent cloud storage platform that is built for the generative age. It offers a better cloud hosting service for your personal files, with features such as AI-enabled multimodal search, customizable layouts, dynamic collections, and one-click asset conversion. Poly is also designed to support outputs from your preferred generative AI models, including Automatic1111, ComfyUI, DALL-E, and Midjourney. With Poly, you can browse, manage, and navigate all your media generated by AI, and seamlessly connect and auto-import your files from your favorite apps.
EtsyHunt
EtsyHunt is an AI-powered platform designed to assist Etsy sellers in improving their shop ranking and visibility. With a comprehensive set of tools for product research, keyword analysis, shop optimization, and competitor tracking, EtsyHunt offers valuable insights and solutions to enhance the efficiency of Etsy operations. The platform boasts the world's largest database of ecommerce products, including millions of Etsy products, tags, and shops. By leveraging AI technology, EtsyHunt empowers sellers to make data-driven decisions and stay ahead in the competitive Etsy marketplace.
Zivy
Zivy is an AI-powered communication tool designed to help Engineering and Product Leads manage and prioritize messages effectively. It transforms the chaotic Slack environment into organized stacks of cards, ensuring that users focus on what truly matters. Zivy's AI capabilities learn user preferences, prioritize important messages, and continuously improve efficiency. The application also emphasizes data security, encrypting messages, and adhering to strict privacy standards. Zivy aims to streamline communication processes and enhance productivity by reducing noise and optimizing message delivery.
20 - Open Source AI Tools
json_repair
This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition:
airflow-chart
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
OmniGibson
OmniGibson is a platform for accelerating Embodied AI research built upon NVIDIA's Omniverse platform. It features photorealistic visuals, physical realism, fluid and soft body support, large-scale high-quality scenes and objects, dynamic kinematic and semantic object states, mobile manipulator robots with modular controllers, and an OpenAI Gym interface. The platform provides a comprehensive environment for researchers to conduct experiments and simulations in the field of Embodied AI.
spear
SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.
airflow-client-python
The Apache Airflow Python Client provides a range of REST API endpoints for managing Airflow metadata objects. It supports CRUD operations for resources, with endpoints accepting and returning JSON. Users can create, read, update, and delete resources. The API design follows conventions with consistent naming and field formats. Update mask is available for patch endpoints to specify fields for update. API versioning is not synchronized with Airflow releases, and changes go through a deprecation phase. The tool supports various authentication methods and error responses follow RFC 7807 format.
langchain-decorators
LangChain Decorators is a layer on top of LangChain that provides syntactic sugar for writing custom langchain prompts and chains. It offers a more pythonic way of writing code, multiline prompts without breaking code flow, IDE support for hinting and type checking, leveraging LangChain ecosystem, support for optional parameters, and sharing parameters between prompts. It simplifies streaming, automatic LLM selection, defining custom settings, debugging, and passing memory, callback, stop, etc. It also provides functions provider, dynamic function schemas, binding prompts to objects, defining custom settings, and debugging options. The project aims to enhance the LangChain library by making it easier to use and more efficient for writing custom prompts and chains.
recognize
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.
spear
SPEAR is a Simulator for Photorealistic Embodied AI Research that addresses limitations in existing simulators by offering 300 unique virtual indoor environments with detailed geometry, photorealistic materials, and unique floor plans. It provides an OpenAI Gym interface for interaction via Python, released under an MIT License. The simulator was developed with support from the Intelligent Systems Lab at Intel and Kujiale.
learnopencv
LearnOpenCV is a repository containing code for Computer Vision, Deep learning, and AI research articles shared on the blog LearnOpenCV.com. It serves as a resource for individuals looking to enhance their expertise in AI through various courses offered by OpenCV. The repository includes a wide range of topics such as image inpainting, instance segmentation, robotics, deep learning models, and more, providing practical implementations and code examples for readers to explore and learn from.
langfun
Langfun is a Python library that aims to make language models (LM) fun to work with. It enables a programming model that flows naturally, resembling the human thought process. Langfun emphasizes the reuse and combination of language pieces to form prompts, thereby accelerating innovation. Unlike other LM frameworks, which feed program-generated data into the LM, langfun takes a distinct approach: It starts with natural language, allowing for seamless interactions between language and program logic, and concludes with natural language and optional structured output. Consequently, langfun can aptly be described as Language as functions, capturing the core of its methodology.
airwin2rack
The 'airwin2rack' repository is a collection of Airwindows audio plugins presented in various formats, including as a static library, a module for VCV Rack, and as CLAP/VST3/AU/LV2/Standalone plugins for DAWs. Users can access these plugins through different methods and interfaces, such as a uniform registry and access pattern, making it easy to integrate Airwindows plugins into their audio projects. The repository also provides instructions for updating the Airwindows sub-library and information on licensing, ensuring that users can utilize the plugins in both open and closed source environments.
executorch
ExecuTorch is an end-to-end solution for enabling on-device inference capabilities across mobile and edge devices including wearables, embedded devices and microcontrollers. It is part of the PyTorch Edge ecosystem and enables efficient deployment of PyTorch models to edge devices. Key value propositions of ExecuTorch are: * **Portability:** Compatibility with a wide variety of computing platforms, from high-end mobile phones to highly constrained embedded systems and microcontrollers. * **Productivity:** Enabling developers to use the same toolchains and SDK from PyTorch model authoring and conversion, to debugging and deployment to a wide variety of platforms. * **Performance:** Providing end users with a seamless and high-performance experience due to a lightweight runtime and utilizing full hardware capabilities such as CPUs, NPUs, and DSPs.
deep-chat
Deep Chat is a fully customizable AI chat component that can be injected into your website with minimal to no effort. Whether you want to create a chatbot that leverages popular APIs such as ChatGPT or connect to your own custom service, this component can do it all! Explore deepchat.dev to view all of the available features, how to use them, examples and more!
animal-ai
Animal-Artificial Intelligence (Animal-AI) is an interdisciplinary research platform designed to understand human, animal, and artificial cognition. It supports AI research to unlock cognitive capabilities and explore the space of possible minds. The open-source project facilitates testing across animals, humans, and AI, providing a comprehensive AI environment with a library of 900 tasks. It offers compatibility with Windows, Linux, and macOS, supporting Python 3.6.x and above. The environment utilizes Unity3D Game Engine, Unity ML-Agents toolkit, and provides interactive elements for AI training scenarios.
aistore
AIStore is a lightweight object storage system designed for AI applications. It is highly scalable, reliable, and easy to use. AIStore can be deployed on any commodity hardware, and it can be used to store and manage large datasets for deep learning and other AI applications.
obs-urlsource
The URL/API Source is a plugin for OBS Studio that allows users to add a media source fetching data from a URL or API endpoint and displaying it as text. It supports input and output templating, various request types, output parsing (JSON, XML/HTML, Regex, CSS selectors), live data updating, output styling, and formatting. Future features include authentication, websocket support, more parsing options, request types, and output formats. The plugin is cross-platform compatible and actively maintained by the developer. Users can support the project on GitHub.
RAGMeUp
RAG Me Up is a generic framework that enables users to perform Retrieve and Generate (RAG) on their own dataset easily. It consists of a small server and UIs for communication. Best run on GPU with 16GB vRAM. Users can combine RAG with fine-tuning using LLaMa2Lang repository. The tool allows configuration for LLM, data, LLM parameters, prompt, and document splitting. Funding is sought to democratize AI and advance its applications.
aicsimageio
AICSImageIO is a Python tool for Image Reading, Metadata Conversion, and Image Writing for Microscopy Images. It supports various file formats like OME-TIFF, TIFF, ND2, DV, CZI, LIF, PNG, GIF, and Bio-Formats. Users can read and write metadata and imaging data, work with different file systems like local paths, HTTP URLs, s3fs, and gcsfs. The tool provides functionalities for full image reading, delayed image reading, mosaic image reading, metadata reading, xarray coordinate plane attachment, cloud IO support, and saving to OME-TIFF. It also offers benchmarking and developer resources.
12 - OpenAI Gpts
Alt Tag Ace for Products
Professional, welcoming creator of detailed, SEO-optimized Alt Tags, specifically for products.
Blog Post Meta Tag Generator
Expert in creating concise, SEO-friendly meta tags for blog posts.
GPT URL Tracking Tag Wizard
Interactive step-by-step UTM Tag Generator for marketing campaigns.
Automated AI Prompt Categorizer
Comprehensive categorization and organization for AI Prompts
Graffiti Genius
Engaging and friendly urban graffiti maestro, adept at turning any idea into street art.
Video SEO Optimizer - GPT
Optimizes YouTube SEO, crafts engaging Title, Description, Tags, Keywords advises on Thumbnails, and provides JSON.