The Best 14 data labeling AI Tools & websites - Gneo.AI

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What is the top 10 AI tools for data labeling?

	Core Features	Price	How to use
Annotab Studio	Annotab Studio is a web-based tool for labeling and annotating data, specifically images.		To use Annotab Studio, simply sign up for the beta version and start leveraging its features. Upload your data and easily create annotations by labeling objects in the images. You can track your annotation progress, version control your dataset, and design your own workflow or choose one from the provided library.
BasicAI	BasicAI provides AI-driven training data solutions, including data annotation services and a data labeling platform, to enhance AI and machine learning models.		To use BasicAI, you can leverage their data annotation services or utilize their AI-powered data labeling platform, called BasicAI Cloud. The platform offers features like auto-annotation, object tracking, and scalable labels management. You can collaborate with your team, manage workflows, and ensure quality assurance using BasicAI Cloud.
ConnectGPT	Increase sales and customer satisfaction by providing AI assistants to customers.		To use ConnectGPT, simply join the waitlist and receive 24x7 support for your customers. You can integrate ConnectGPT into your website by using your own API keys and choosing from a variety of AI models from OpenAI, Google, and Meta. Set the personality and intent for your chatbot, train it on your website data or your own conversations, and customize the UI according to your preferences. Get the benefits of white labeling, multiple bots, and API call access in the basic plan, which sets ConnectGPT apart from competitors.
Label Studio	Label Studio: open-source tool for labeling data in various models.		To use Label Studio, you can follow these steps: 1. Install the Label Studio package through pip, brew, or clone the repository from GitHub. 2. Launch Label Studio using the installed package or Docker. 3. Import your data into Label Studio. 4. Choose the data type (images, audio, text, time series, multi-domain, or video) and select the specific labeling task (e.g., image classification, object detection, audio transcription). 5. Start labeling your data using customizable tags and templates. 6. Connect to your ML/AI pipeline and use webhooks, Python SDK, or API for authentication, project management, and model predictions. 7. Explore and manage your dataset in the Data Manager with advanced filters. 8. Support multiple projects, use cases, and users within the Label Studio platform.
LayerNext	LayerNext is an AI data management platform for Computer Vision data.		To use LayerNext, start by signing up and creating an account. You can then explore and visualize all your AI data in one place using the DataLake feature. The Annotation Studio allows you to label image and video data at scale, while the Dataset Manager helps you manage training datasets with version control. LayerNext can be seamlessly integrated with any computer vision application or infrastructure through its SDK and API. Additionally, you can automate computer vision pipelines and optimize productivity through purpose-built data tools and automated workflows.
Lettria	"Lettria is a no-code AI platform that helps users structure and analyze text data effectively."		To use Lettria, you can start by signing up for a free account on the platform. Once logged in, you can access Lettria's various NLP features such as text collection and management, text cleaning, text labeling, dictionary management, taxonomy management, and ontology management. You can also train and evaluate NLP models using Lettria's AutoLettria tool. Lettria's platform is designed to be user-friendly and does not require any coding knowledge. Simply follow the intuitive interface and utilize the available features to process and analyze your text data.
Dioptra	Dioptra is an open source platform for data curation and management in computer vision and NLP.		1. Curate the most valuable unlabeled data to improve domain coverage and model performance. 2. Register your metadata to Dioptra to ensure your data remains with you. 3. Diagnose root cause model failure modes and regressions using Dioptra's data centric toolkit. 4. Use active learning miners to sample the most valuable unlabeled data. 5. Integrate with your labeling and retraining stack using Dioptra's APIs.
Lobe	"Lobe is a user-friendly app for training and integrating custom machine learning models."		To use Lobe, simply download the app on your Mac or Windows computer. Collect and label your images or data to create a machine learning dataset. Lobe automatically trains your model based on the labeled examples. You can then use your trained model with your webcam or images, improve its predictions, and finally export it to your app for deployment.
https://peoplefor.ai/	People for AI offers high-quality data labeling services using experienced labelers and advanced tools.		To use People for AI's data labeling services, you need to contact them through their website or by emailing them. They will assign you a project manager who will work with you to understand your project requirements and define the data labeling strategy. Once the strategy is finalized, their expert labelers will start labeling your dataset using their specialized tools. Throughout the project, they provide regular communication and progress updates to ensure your satisfaction with the results.
PromptLoop	Summary: PromptLoop is a versatile AI tool for data processing and web research in Google Sheets and Excel.		To use PromptLoop, simply install the plug-in and integrate it into your spreadsheet software. You can then access the AI models directly within your spreadsheets to perform tasks such as intelligent tagging, labeling, analysis, web research, and content quality analysis. It also allows you to train and utilize custom AI models specific to your data needs. PromptLoop offers a user-friendly interface that makes it easy for anyone to extract valuable insights from complex information.

FAQ about data labeling

What is data labeling?: Data labeling is the process of adding meaningful labels or tags to raw data to make it understandable and usable for machine learning and AI applications.

Why is data labeling important for AI?: Data labeling is essential for AI because it provides the necessary training data for machine learning models to learn and make accurate predictions or decisions.

What are some common types of data labeling?: Common types of data labeling include image classification, object detection, semantic segmentation, text categorization, sentiment analysis, and entity recognition.

How much data needs to be labeled for AI?: The amount of labeled data required depends on the complexity of the AI task and the desired performance level. Generally, more complex tasks and higher accuracy requirements need larger labeled datasets.

Can data labeling be automated?: While some data labeling tasks can be partially automated using techniques like pre-labeling or active learning, human input is still necessary for quality control and handling edge cases.

What are some best practices for data labeling?: Best practices for data labeling include defining clear labeling guidelines, ensuring data diversity and representativeness, implementing quality control measures, and using standardized labeling formats and tools.