Best AI tools for< Classification >

Text Generator is an AI-powered text generation tool that provides users with accurate, fast, and flexible text generation capabilities. With its advanced large neural networks, Text Generator offers a cost-effective solution for various text-related tasks. The tool's intuitive 'prompt engineering' feature allows users to guide text creation by providing keywords and natural questions, making it adaptable for tasks such as classification and sentiment analysis. Text Generator ensures industry-leading security by never storing personal information on its servers. The tool's continuous training ensures that its AI remains up-to-date with the latest events. Additionally, Text Generator offers a range of features including speech-to-text API, text-to-speech API, and code generation, supporting multiple spoken languages and programming languages. With its one-line migration from OpenAI's text generation hub and a shared embedding for multiple spoken languages, images, and code, Text Generator empowers users with powerful search, fingerprinting, tracking, and classification capabilities.

Pointly is an intelligent, cloud-based B2B software solution that enables efficient automatic and advanced manual classification in 3D point clouds. It offers innovative AI techniques for fast and precise data classification and vectorization, transforming point cloud analysis into an enjoyable and efficient workflow. Pointly provides standard and custom classifiers, tools for classification and vectorization, API and on-premise classification options, collaboration features, secure cloud processing, and scalability for handling large-scale point cloud data.

Taylor is a deterministic AI tool that empowers Business & Engineering teams to enrich and automate text data at scale. It allows users to structure freeform text, customize enrichments, and build classification models for real-time data pipelines. With easy customization and integration capabilities, Taylor brings powerful machine learning to streamline business operations and product features.

Neuwo is a leading contextual AI engine for content classification and automated tagging. It advocates that organizations of any size should have access to cutting-edge yet cost-effective contextual AI technology for meaningful automated content categorization and customization. The platform enables users to transition seamlessly from Oracle Grapeshot to Neuwo, offering advanced AI-driven algorithms for more precise targeting and increased engagement. Neuwo's technology ensures reaching the right audience at the right time, without the need for contracts. The platform enriches valuable data through intelligent content processing, brand safety, and suitability, and content activation, adding value to digital properties and businesses.

Tinq.ai is a natural language processing (NLP) tool that provides a range of text analysis capabilities through its API. It offers tools for tasks such as plagiarism checking, text summarization, sentiment analysis, named entity recognition, and article extraction. Tinq.ai's API can be integrated into applications to add NLP functionality, such as content moderation, sentiment analysis, and text rewriting.

Convr is an AI-driven underwriting analysis platform that helps commercial P&C insurance organizations transform their underwriting operations. It provides a modularized AI underwriting and intelligent document automation workbench that enriches and expedites the commercial insurance new business and renewal submission flow with underwriting insights, business classification, and risk scoring. Convr's mission is to solve the last big problem of commercial insurance while improving profitability and increasing efficiency.

Convr is a modularized AI underwriting and intelligent document automation workbench that enriches and expedites the commercial insurance new business and renewal submission flow with underwriting insights, business classification and risk scoring. As a trusted technology partner and advisor with deep industry expertise, we help insurance organizations transform their underwriting operations through our AI-driven digital underwriting analysis platform.

ImageSorter.io is a free online tool designed to help users sort and organize their images efficiently. Users can easily select and add images to the tool, then use simple drag-and-drop functionality to rearrange them. The tool also offers a Pro version with additional features such as setting confidence thresholds and sorting images by tag order. ImageSorter.io provides a user-friendly interface that simplifies the image organization process, making it a valuable tool for individuals and professionals alike.

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.

Landing AI is a computer vision platform and AI software company that provides a cloud-based platform for building and deploying computer vision applications. The platform includes a library of pre-trained models, a set of tools for data labeling and model training, and a deployment service that allows users to deploy their models to the cloud or edge devices. Landing AI's platform is used by a variety of industries, including automotive, electronics, food and beverage, medical devices, life sciences, agriculture, manufacturing, infrastructure, and pharma.

NVIDIA is a world leader in artificial intelligence computing. The company's products and services are used by businesses and governments around the world to develop and deploy AI applications. NVIDIA's AI platform includes hardware, software, and tools that make it easy to build and train AI models. The company also offers a range of cloud-based AI services that make it easy to deploy and manage AI applications. NVIDIA's AI platform is used in a wide variety of industries, including healthcare, manufacturing, retail, and transportation. The company's AI technology is helping to improve the efficiency and accuracy of a wide range of tasks, from medical diagnosis to product design.

TensorFlow is an end-to-end platform for machine learning. It provides a wide range of tools and resources to help developers build, train, and deploy ML models. TensorFlow is used by researchers and developers all over the world to solve real-world problems in a variety of domains, including computer vision, natural language processing, and robotics.

Grok-1.5 Vision (Grok-1.5V) is a groundbreaking multimodal AI model developed by Elon Musk's research lab, x.AI. This advanced model has the potential to revolutionize the field of artificial intelligence and shape the future of various industries. Grok-1.5V combines the capabilities of computer vision, natural language processing, and other AI techniques to provide a comprehensive understanding of the world around us. With its ability to analyze and interpret visual data, Grok-1.5V can assist in tasks such as object recognition, image classification, and scene understanding. Additionally, its natural language processing capabilities enable it to comprehend and generate human language, making it a powerful tool for communication and information retrieval. Grok-1.5V's multimodal nature sets it apart from traditional AI models, allowing it to handle complex tasks that require a combination of visual and linguistic understanding. This makes it a valuable asset for applications in fields such as healthcare, manufacturing, and customer service.

Meta AI is a research lab dedicated to advancing the field of artificial intelligence. Our mission is to build foundational AI technologies that will solve some of the world's biggest challenges, such as climate change, disease, and poverty.

Keras is an open-source deep learning API written in Python, designed to make building and training deep learning models easier. It provides a user-friendly interface and a wide range of features and tools to help developers create and deploy machine learning applications. Keras is compatible with multiple frameworks, including TensorFlow, Theano, and CNTK, and can be used for a variety of tasks, including image classification, natural language processing, and time series analysis.

fast.ai is a non-profit organization that provides free online courses and resources on deep learning and artificial intelligence. The organization was founded in 2016 by Jeremy Howard and Rachel Thomas, and has since grown to a community of over 100,000 learners from all over the world. fast.ai's mission is to make deep learning accessible to everyone, regardless of their background or experience. The organization's courses are taught by leading experts in the field, and are designed to be practical and hands-on. fast.ai also offers a variety of resources to help learners get started with deep learning, including a forum, a wiki, and a blog.

Gradio is a tool that allows users to quickly and easily create web-based interfaces for their machine learning models. With Gradio, users can share their models with others, allowing them to interact with and use the models remotely. Gradio is easy to use and can be integrated with any Python library. It can be used to create a variety of different types of interfaces, including those for image classification, natural language processing, and time series analysis.

Amazon Science is a research and development organization within Amazon that focuses on developing new technologies and products in the fields of artificial intelligence, machine learning, and computer science. The organization is home to a team of world-renowned scientists and engineers who are working on a wide range of projects, including developing new algorithms for machine learning, building new computer vision systems, and creating new natural language processing tools. Amazon Science is also responsible for developing new products and services that use these technologies, such as the Amazon Echo and the Amazon Fire TV.

Teachable Machine is a web-based tool that makes it easy to create custom machine learning models, even if you don't have any coding experience. With Teachable Machine, you can train models to recognize images, sounds, and poses. Once you've trained a model, you can export it to use in your own projects.

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The online version of the book is now complete and will remain available online for free. The deep learning textbook can now be ordered on Amazon. For up to date announcements, join our mailing list.

This tool is designed to detect and classify Alzheimer's Disease using Deep Learning and Machine Learning algorithms on an early basis, which is further optimized using the Crow Search Algorithm (CSA). Alzheimer's is a fatal disease, and early detection is crucial for patients to predetermine their condition and prevent its progression. By analyzing MRI scanned images using Artificial Intelligence technology, this tool can classify patients who may or may not develop AD in the future. The CSA algorithm, combined with ML algorithms, has proven to be the most effective approach for this purpose.

The .NET AI Workshop is a comprehensive guide designed to help developers add AI features to .NET applications. It covers various AI-based features such as classification, summarization, data extraction/cleaning, anomaly detection, translation, sentiment detection, semantic search, Q&A chatbots, and voice assistants. The workshop is tailored for developers new to AI in .NET applications, focusing on the usage of AI services without the need for prior AI technology knowledge. It provides examples using .NET and C# with a focus on AI topics, aiming to enhance productivity and automation in applications.

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 Clarifai Python SDK offers a comprehensive set of tools to integrate Clarifai's AI platform to leverage computer vision capabilities like classification , detection ,segementation and natural language capabilities like classification , summarisation , generation , Q&A ,etc into your applications. With just a few lines of code, you can leverage cutting-edge artificial intelligence to unlock valuable insights from visual and textual content.

LLM-Tuning is a collection of tools and resources for fine-tuning large language models (LLMs). It includes a library of pre-trained LoRA models, a set of tutorials and examples, and a community forum for discussion and support. LLM-Tuning makes it easy to fine-tune LLMs for a variety of tasks, including text classification, question answering, and dialogue generation. With LLM-Tuning, you can quickly and easily improve the performance of your LLMs on downstream tasks.

CAPPr is a tool for text classification that does not require training or post-processing. It allows users to have their language models pick from a list of choices or compute the probability of a completion given a prompt. The tool aims to help users get more out of open source language models by simplifying the text classification process. CAPPr can be used with GGUF models, Hugging Face models, models from the OpenAI API, and for tasks like caching instructions, extracting final answers from step-by-step completions, and running predictions in batches with different sets of completions.

Fastc is a tool focused on CPU execution, using efficient models for embedding generation and cosine similarity classification. It allows for efficient multi-classifier execution without extra overhead. Users can easily train text classifiers, export models, publish to HuggingFace, load existing models, make class predictions, use instruct templates, and launch an inference server. The tool provides an HTTP API for text classification with JSON payloads and supports multiple languages for language identification.

Actual AI is a project designed to categorize uncategorized transactions for Actual Budget using OpenAI or OpenAI specification compatible API. It sends requests to the OpenAI API to classify transactions based on their description, amount, and notes. Transactions that cannot be classified are marked as 'not guessed' in notes. The tool allows users to sync accounts before classification and classify transactions on a cron schedule. Guessed transactions are marked in notes for easy review.

HuaTuoAI is an artificial intelligence image classification system specifically designed for traditional Chinese medicine. It utilizes deep learning techniques, such as Convolutional Neural Networks (CNN), to accurately classify Chinese herbs and ingredients based on input images. The project aims to unlock the secrets of plants, depict the unknown realm of Chinese medicine using technology and intelligence, and perpetuate ancient cultural heritage.

ztachip is a RISCV accelerator designed for vision and AI edge applications, offering up to 20-50x acceleration compared to non-accelerated RISCV implementations. It features an innovative tensor processor hardware to accelerate various vision tasks and TensorFlow AI models. ztachip introduces a new tensor programming paradigm for massive processing/data parallelism. The repository includes technical documentation, code structure, build procedures, and reference design examples for running vision/AI applications on FPGA devices. Users can build ztachip as a standalone executable or a micropython port, and run various AI/vision applications like image classification, object detection, edge detection, motion detection, and multi-tasking on supported hardware.

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.

Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence classification models. TEI enables high-performance extraction for popular models like FlagEmbedding, Ember, GTE, and E5. It implements features such as no model graph compilation step, Metal support for local execution on Macs, small docker images with fast boot times, token-based dynamic batching, optimized transformers code for inference using Flash Attention, Candle, and cuBLASLt, Safetensors weight loading, and production-ready features like distributed tracing with Open Telemetry and Prometheus metrics.

eShopSupport is a sample .NET application showcasing common use cases and development practices for building AI solutions in .NET, specifically Generative AI. It demonstrates a customer support application for an e-commerce website using a services-based architecture with .NET Aspire. The application includes support for text classification, sentiment analysis, text summarization, synthetic data generation, and chat bot interactions. It also showcases development practices such as developing solutions locally, evaluating AI responses, leveraging Python projects, and deploying applications to the Cloud.

FastFit is a Python package designed for fast and accurate few-shot classification, especially for scenarios with many semantically similar classes. It utilizes a novel approach integrating batch contrastive learning and token-level similarity score, significantly improving multi-class classification performance in speed and accuracy across various datasets. FastFit provides a convenient command-line tool for training text classification models with customizable parameters. It offers a 3-20x improvement in training speed, completing training in just a few seconds. Users can also train models with Python scripts and perform inference using pretrained models for text classification tasks.

PyCM is a Python library for multi-class confusion matrices, providing support for input data vectors and direct matrices. It is a comprehensive tool for post-classification model evaluation, offering a wide range of metrics for predictive models and accurate evaluation of various classifiers. PyCM is designed for data scientists who require diverse metrics for their models.

Ultralytics YOLO for Flutter is a Flutter plugin that allows you to integrate Ultralytics YOLO computer vision models into your mobile apps. It supports both Android and iOS platforms, providing APIs for object detection and image classification. The plugin leverages Flutter Platform Channels for seamless communication between the client and host, handling all processing natively. Before using the plugin, you need to export the required models in `.tflite` and `.mlmodel` formats. The plugin provides support for tasks like detection and classification, with specific instructions for Android and iOS platforms. It also includes features like camera preview and methods for object detection and image classification on images. Ultralytics YOLO thrives on community collaboration and offers different licensing paths for open-source and commercial use cases.

Fishial.ai is a project focused on training and validating scripts for fish segmentation and classification models. It includes various scripts for automatic training with different loss functions, dataset manipulation, and model setup using Detectron2 API. The project also provides tools for converting classification models to TorchScript format and creating training datasets. The models available include MaskRCNN for fish segmentation and various versions of ResNet18 for fish classification with different class counts and features. The project aims to facilitate fish identification and analysis through machine learning techniques.

This repository contains code for the AI challenger competition on plant disease detection. The goal is to classify nearly 50,000 plant leaf photos into 61 categories based on 'species-disease-severity'. The framework used is Keras with TensorFlow backend, implementing DenseNet for image classification. Data is uploaded to a private dataset on Kaggle for model training. The code includes data preparation, model training, and prediction steps.

Benchmark various LLM Structured Output frameworks like Instructor, Mirascope, Langchain, LlamaIndex, Fructose, Marvin, Outlines, LMFormatEnforcer, etc on tasks like multi-label classification, named entity recognition, synthetic data generation. The tool provides benchmark results, methodology, instructions to run the benchmark, add new data, and add a new framework. It also includes a roadmap for framework-related tasks, contribution guidelines, citation information, and feedback request.

Intel® Extension for Transformers is an innovative toolkit designed to accelerate GenAI/LLM everywhere with the optimal performance of Transformer-based models on various Intel platforms, including Intel Gaudi2, Intel CPU, and Intel GPU. The toolkit provides the below key features and examples: * Seamless user experience of model compressions on Transformer-based models by extending [Hugging Face transformers](https://github.com/huggingface/transformers) APIs and leveraging [Intel® Neural Compressor](https://github.com/intel/neural-compressor) * Advanced software optimizations and unique compression-aware runtime (released with NeurIPS 2022's paper [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) and [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114), and NeurIPS 2021's paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754)) * Optimized Transformer-based model packages such as [Stable Diffusion](examples/huggingface/pytorch/text-to-image/deployment/stable_diffusion), [GPT-J-6B](examples/huggingface/pytorch/text-generation/deployment), [GPT-NEOX](examples/huggingface/pytorch/language-modeling/quantization#2-validated-model-list), [BLOOM-176B](examples/huggingface/pytorch/language-modeling/inference#BLOOM-176B), [T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), [Flan-T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), and end-to-end workflows such as [SetFit-based text classification](docs/tutorials/pytorch/text-classification/SetFit_model_compression_AGNews.ipynb) and [document level sentiment analysis (DLSA)](workflows/dlsa) * [NeuralChat](intel_extension_for_transformers/neural_chat), a customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of [plugins](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat/docs/advanced_features.md) such as [Knowledge Retrieval](./intel_extension_for_transformers/neural_chat/pipeline/plugins/retrieval/README.md), [Speech Interaction](./intel_extension_for_transformers/neural_chat/pipeline/plugins/audio/README.md), [Query Caching](./intel_extension_for_transformers/neural_chat/pipeline/plugins/caching/README.md), and [Security Guardrail](./intel_extension_for_transformers/neural_chat/pipeline/plugins/security/README.md). This framework supports Intel Gaudi2/CPU/GPU. * [Inference](https://github.com/intel/neural-speed/tree/main) of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels for Intel CPU and Intel GPU (TBD), supporting [GPT-NEOX](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox), [LLAMA](https://github.com/intel/neural-speed/tree/main/neural_speed/models/llama), [MPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/mpt), [FALCON](https://github.com/intel/neural-speed/tree/main/neural_speed/models/falcon), [BLOOM-7B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/bloom), [OPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/opt), [ChatGLM2-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/chatglm), [GPT-J-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptj), and [Dolly-v2-3B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox). Support AMX, VNNI, AVX512F and AVX2 instruction set. We've boosted the performance of Intel CPUs, with a particular focus on the 4th generation Intel Xeon Scalable processor, codenamed [Sapphire Rapids](https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors.html).

Just upload a numerical dataset for classification task, will apply data analysis and machine learning steps to make a best model possible.

Anime Classification Expert

A bilingual porcelain classification assistant.

GICS is a classification standard developed by MSCI and S&P Dow Jones Indices. This GPT is not a MSCI and S&P product. Official website : https://www.msci.com/our-solutions/indexes/gics

Thoracolumbar Injury Classification and Severity (TLICS) system calculator

Import Export Compliance | Tariff Classification | Shipping Queries | Logistics & Supply Chain Solutions

NACE (Nomenclature of Economic Activities) is the European statistical classification of economic activities. This is not an official product. Official information here: https://nacev2.com/en

Assists with DGL coding, focusing on edge classification and link prediction.

Informative guide on lymphoma, covering classification and treatments.

Classifies articles into knowledge domains. source code: https://homun.posetmage.com/Agents/

Guiding you through the depths of deep learning with accuracy and respect.

Does it only do Hot Dog? --And Not Hot Dog.

Expert in computer vision, deep learning, ready to assist you with 3d and geometric computer vision. https://github.com/kornia/pixie

BIAN v12.0

Professional guide in computer vision, offering clear, insightful advice.

Describes images with bilingual titles/keywords.

Super astronomer identifying clouds in English and Chinese, sharing facts in Chinese.

Expert in UNSPSC Codes (United Nations Standard Products and Services Code®).

Enthusiastic and factual animal identifier with engaging facts.

Comprehensive categorization and organization for AI Prompts