Best AI tools for< Image Annotator >

CVAT is an open-source data annotation platform that helps teams of any size annotate data for machine learning. It is used by companies big and small in a variety of industries, including healthcare, retail, and automotive. CVAT is known for its intuitive user interface, advanced features, and support for a wide range of data formats. It is also highly extensible, allowing users to add their own custom features and integrations.

Patee.io is an AI-powered platform that helps businesses automate their data annotation and labeling tasks. With Patee.io, businesses can easily create, manage, and annotate large datasets, which can then be used to train machine learning models. Patee.io offers a variety of features that make it easy to annotate data, including a user-friendly interface, a variety of annotation tools, and the ability to collaborate with others. Patee.io also offers a number of pre-built models that can be used to automate the annotation process, saving businesses time and money.

Cogniroot is an AI-powered platform that helps businesses automate their data annotation and data labeling processes. It provides a suite of tools and services that make it easy for businesses to train their machine learning models with high-quality data. Cogniroot's platform is designed to be scalable, efficient, and cost-effective, making it a valuable tool for businesses of all sizes.

LightPDF is an AI-powered, free online PDF editor, converter, and reader. It offers a wide range of PDF tools, including the ability to convert PDFs to and from other formats, edit PDFs, add watermarks, split and merge PDFs, rotate PDFs, annotate PDFs, optimize PDFs, compress PDFs, perform OCR on PDFs, and protect PDFs. LightPDF also offers a variety of AI-powered features, such as an AI chatbot that can answer questions about documents and an AI-powered OCR engine that can convert scanned PDFs and images to text.

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.

SentiSight.ai is a machine learning platform for image recognition solutions, offering services such as object detection, image segmentation, image classification, image similarity search, image annotation, computer vision consulting, and intelligent automation consulting. Users can access pre-trained models, background removal, NSFW detection, text recognition, and image recognition API. The platform provides tools for image labeling, project management, and training tutorials for various image recognition models. SentiSight.ai aims to streamline the image annotation process, empower users to build and train their own models, and deploy them for online or offline use.

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.

Innovatiana is a data labeling outsourcing company that provides high-quality training data for AI models. They specialize in computer vision, data moderation, document processing, natural language processing, and data collection. Innovatiana is committed to ethical and sustainable practices, and they pay their data labelers fair wages and provide them with good working conditions. They also use a variety of quality control measures to ensure that their data is accurate and reliable.

V7 is an AI data engine for computer vision and generative AI. It provides a multimodal automation tool that helps users label data 10x faster, power AI products via API, build AI + human workflows, and reach 99% AI accuracy. V7's platform includes features such as automated annotation, DICOM annotation, dataset management, model management, image annotation, video annotation, document processing, and labeling services.

Clickworker GmbH is an AI training data and data management services platform that leverages a global crowd of Clickworkers to generate, validate, and label data for AI systems. The platform offers a range of AI datasets for machine learning, audio, image, and video datasets, as well as services like image annotation, content editing, and creation. Clickworkers participate in projects on a freelance basis, performing micro-tasks to create high-quality training data tailored to the requirements of AI systems. The platform also provides solutions for industries such as AI and data science research, eCommerce, fashion, retail, and digital marketing.

Globose Technology Solutions Pvt Ltd (GTS) is an AI data collection company that provides various datasets such as image datasets, video datasets, text datasets, speech datasets, etc., to train machine learning models. They offer premium data collection services with a human touch, aiming to refine AI vision and propel AI forward. With over 25+ years of experience, they specialize in data management, annotation, and effective data collection techniques for AI/ML. The company focuses on unlocking high-quality data, understanding AI's transformative impact, and ensuring data accuracy as the backbone of reliable AI.

Datature is an all-in-one platform for building and deploying computer vision models. It provides tools for data management, annotation, training, and deployment, making it easy to develop and implement computer vision solutions. Datature is used by a variety of industries, including healthcare, retail, manufacturing, and agriculture.

SceneDreamer is an AI tool that specializes in generating unbounded 3D scenes from 2D image collections. It utilizes an unconditional generative model to synthesize large-scale 3D landscapes with diverse styles, 3D consistency, well-defined depth, and free camera trajectory. The tool is learned from in-the-wild 2D image collections without the need for 3D annotations. SceneDreamer's core features include an efficient 3D scene representation, generative scene parameterization, and a neural volumetric renderer for producing photorealistic images.

Roboflow is a platform that provides tools for building and deploying computer vision models. It offers a range of features, including data annotation, model training, and deployment. Roboflow is used by over 250,000 engineers to create datasets, train models, and deploy to production.

Segment Anything by Meta AI is an advanced AI model that specializes in image segmentation, allowing users to easily 'cut out' any object in an image with a single click. The model, known as SAM, offers zero-shot generalization to unfamiliar objects and images without the need for additional training. SAM's promptable design enables a wide range of segmentation tasks through input prompts, making it a versatile tool for various applications.

Viso Suite is a no-code computer vision platform that enables users to build, deploy, and scale computer vision applications. It provides a comprehensive set of tools for data collection, annotation, model training, application development, and deployment. Viso Suite is trusted by leading Fortune Global companies and has been used to develop a wide range of computer vision applications, including object detection, image classification, facial recognition, and anomaly detection.

Innovatiana is a data labeling outsourcing platform that offers high-quality datasets for artificial intelligence models. They specialize in image, audio/video, and text data labeling tasks, providing ethical outsourcing with a focus on impact and transparency. Innovatiana recruits and trains their own team in Madagascar, ensuring fair pay and good working conditions. They offer competitive rates, secure data handling, and high-quality labeled data to feed AI models. The platform supports various AI tasks such as Computer Vision, Data Collection, Data Moderation, Documents Processing, and Natural Language Processing.

Macgence is an AI training data services platform that offers high-quality off-the-shelf structured training data for organizations to build effective AI systems at scale. They provide services such as custom data sourcing, data annotation, data validation, content moderation, and localization. Macgence combines global linguistic, cultural, and technological expertise to create high-quality datasets for AI models, enabling faster time-to-market across the entire model value chain. With more than 5 years of experience, they support and scale AI initiatives of leading global innovators by designing custom data collection programs. Macgence specializes in handling AI training data for text, speech, image, and video data, offering cognitive annotation services to unlock the potential of unstructured textual data.

Toloka AI is a data labeling platform that empowers AI development by combining human insight with machine learning models. It offers adaptive AutoML, human-in-the-loop workflows, large language models, and automated data labeling. The platform supports various AI solutions with human input, such as e-commerce services, content moderation, computer vision, and NLP. Toloka AI aims to accelerate machine learning processes by providing high-quality human-labeled data and leveraging the power of the crowd.

Shaip is a human-powered data processing service specializing in AI and ML models. They offer a wide range of services including data collection, annotation, de-identification, and more. Shaip provides high-quality training data for various AI applications, such as healthcare AI, conversational AI, and computer vision. With over 15 years of expertise, Shaip helps organizations unlock critical information from unstructured data, enabling them to achieve better results in their AI initiatives.

DataDreamer is an advanced toolkit designed to facilitate the development of edge AI models by enabling synthetic data generation, knowledge extraction from pre-trained models, and creation of efficient and potent models. It eliminates the need for extensive datasets by generating synthetic datasets, leverages latent knowledge from pre-trained models, and focuses on creating compact models suitable for integration into any device and performance for specialized tasks. The toolkit offers features like prompt generation, image generation, dataset annotation, and tools for training small-scale neural networks for edge deployment. It provides hardware requirements, usage instructions, available models, and limitations to consider while using the library.

At ZenML, we believe in the importance of annotation and labeling workflows in the machine learning lifecycle. This repository showcases a curated list of open-source data annotation and labeling tools that are actively maintained and fit for purpose. The tools cover various domains such as multi-modal, text, images, audio, video, time series, and other data types. Users can contribute to the list and discover tools for tasks like named entity recognition, data annotation for machine learning, image and video annotation, text classification, sequence labeling, object detection, and more. The repository aims to help users enhance their data-centric workflows by leveraging these tools.

X-AnyLabeling is a robust annotation tool that seamlessly incorporates an AI inference engine alongside an array of sophisticated features. Tailored for practical applications, it is committed to delivering comprehensive, industrial-grade solutions for image data engineers. This tool excels in swiftly and automatically executing annotations across diverse and intricate tasks.

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.

Spark NLP is a state-of-the-art Natural Language Processing library built on top of Apache Spark. It provides simple, performant, and accurate NLP annotations for machine learning pipelines that scale easily in a distributed environment. Spark NLP comes with 36000+ pretrained pipelines and models in more than 200+ languages. It offers tasks such as Tokenization, Word Segmentation, Part-of-Speech Tagging, Named Entity Recognition, Dependency Parsing, Spell Checking, Text Classification, Sentiment Analysis, Token Classification, Machine Translation, Summarization, Question Answering, Table Question Answering, Text Generation, Image Classification, Image to Text (captioning), Automatic Speech Recognition, Zero-Shot Learning, and many more NLP tasks. Spark NLP is the only open-source NLP library in production that offers state-of-the-art transformers such as BERT, CamemBERT, ALBERT, ELECTRA, XLNet, DistilBERT, RoBERTa, DeBERTa, XLM-RoBERTa, Longformer, ELMO, Universal Sentence Encoder, Llama-2, M2M100, BART, Instructor, E5, Google T5, MarianMT, OpenAI GPT2, Vision Transformers (ViT), OpenAI Whisper, and many more not only to Python and R, but also to JVM ecosystem (Java, Scala, and Kotlin) at scale by extending Apache Spark natively.

DataChain is an open-source Python library for processing and curating unstructured data at scale. It supports AI-driven data curation using local ML models and LLM APIs, handles large datasets, and is Python-friendly with Pydantic objects. It excels at optimizing batch operations and is designed for offline data processing, curation, and ETL. Typical use cases include Computer Vision data curation, LLM analytics, and validation.

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.

Cleanlab helps you **clean** data and **lab** els by automatically detecting issues in a ML dataset. To facilitate **machine learning with messy, real-world data** , this data-centric AI package uses your _existing_ models to estimate dataset problems that can be fixed to train even _better_ models.

Detoxify is a library that provides trained models and code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. It includes models like 'original', 'unbiased', and 'multilingual' trained on different datasets to detect toxicity and minimize bias. The library aims to help in stopping harmful content online by interpreting visual content in context. Users can fine-tune the models on carefully constructed datasets for research purposes or to aid content moderators in flagging out harmful content quicker. The library is built to be user-friendly and straightforward to use.

MMMU is a benchmark designed to evaluate multimodal models on college-level subject knowledge tasks, covering 30 subjects and 183 subfields with 11.5K questions. It focuses on advanced perception and reasoning with domain-specific knowledge, challenging models to perform tasks akin to those faced by experts. The evaluation of various models highlights substantial challenges, with room for improvement to stimulate the community towards expert artificial general intelligence (AGI).

MathVerse is an all-around visual math benchmark designed to evaluate the capabilities of Multi-modal Large Language Models (MLLMs) in visual math problem-solving. It collects high-quality math problems with diagrams to assess how well MLLMs can understand visual diagrams for mathematical reasoning. The benchmark includes 2,612 problems transformed into six versions each, contributing to 15K test samples. It also introduces a Chain-of-Thought (CoT) Evaluation strategy for fine-grained assessment of output answers.

The LLM Evaluation guidebook provides comprehensive guidance on evaluating language model performance, including different evaluation methods, designing evaluations, and practical tips. It caters to both beginners and advanced users, offering insights on model inference, tokenization, and troubleshooting. The guide covers automatic benchmarks, human evaluation, LLM-as-a-judge scenarios, troubleshooting practicalities, and general knowledge on LLM basics. It also includes planned articles on automated benchmarks, evaluation importance, task-building considerations, and model comparison challenges. The resource is enriched with recommended links and acknowledgments to contributors and inspirations.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

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.

This repository is a collection of papers on leveraging Large Language Models (LLMs) in Graph Tasks. It provides a comprehensive overview of how LLMs can enhance graph-related tasks by combining them with traditional Graph Neural Networks (GNNs). The integration of LLMs with GNNs allows for capturing both structural and contextual aspects of nodes in graph data, leading to more powerful graph learning. The repository includes summaries of various models that leverage LLMs to assist in graph-related tasks, along with links to papers and code repositories for further exploration.

Data Prep Kit is a community project aimed at democratizing and speeding up unstructured data preparation for LLM app developers. It provides high-level APIs and modules for transforming data (code, language, speech, visual) to optimize LLM performance across different use cases. The toolkit supports Python, Ray, Spark, and Kubeflow Pipelines runtimes, offering scalability from laptop to datacenter-scale processing. Developers can contribute new custom modules and leverage the data processing library for building data pipelines. Automation features include workflow automation with Kubeflow Pipelines for transform execution.

The 'awesome-sound_event_detection' repository is a curated reading list focusing on sound event detection and Sound AI. It includes research papers covering various sub-areas such as learning formulation, network architecture, pooling functions, missing or noisy audio, data augmentation, representation learning, multi-task learning, few-shot learning, zero-shot learning, knowledge transfer, polyphonic sound event detection, loss functions, audio and visual tasks, audio captioning, audio retrieval, audio generation, and more. The repository provides a comprehensive collection of papers, datasets, and resources related to sound event detection and Sound AI, making it a valuable reference for researchers and practitioners in the field.

Describes images with bilingual titles/keywords.

A visual analysis expert who guesses image locations.

Confirms image uploads without analysis or detail.

Creating unique, visually stunning images of baked delights.

I create variations on your image themes.

Just send us an image of a scene from a video work and i will guess the name of the work!

More accurate and easier image generation with self critique & improvement! Try it now

Question-and-answer style image design agent, solving the problem of not knowing how to describe design parameters to GPT.

Highly optimized prompts and top secret refinements to create the perfect image every time...

Efficiently generates images and overlays 'LGTM'

A GPT to create images, and keep track of metadata of the images

A comprehensive guide for finding themed public domain images with a vast resource list.

Expert in Tailored Image Prompts

Geneate an image ➡ Request modifications. This GPT supports generating consistent and continuous images with Dalle. It also offers the ability to restore or integrate photos you upload. ✔️Where to use: Wordpress Blog Post, Youtube thumbnail, AI profile, facebook, X, threads feed, Instagram reels

What do you want to see?

Creates warm, gentle, ethereal images for X posts.

画像中の文章を日本語に翻訳します。（使い方：画像をアップロードするだけ。プロンプトの文章は不要です。）　2023/12/29 より自然な日本語になるように修正

Type “Start” and Get Exact Details on Image Generation and/or Duplication

Stable Diffusion Prompt Word

Determines if images are AI-generated or real, and learns from feedback.