Best AI tools for< Semantic Segmentation >

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.

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.

Semantic Scholar is a free, AI-powered research tool for scientific literature. It is based at the Allen Institute for AI and provides access to over 217 million papers from all fields of science. Semantic Scholar uses AI to help users discover and explore scientific literature, and to stay up-to-date on the latest research. The tool also includes a number of features to help users manage their research, such as the ability to save papers, create bibliographies, and share research with others.

Biblos Semantic Bible Search & Summary is an AI-powered tool that offers a powerful Bible search experience. It provides semantic search capabilities and a powerful understanding model to enhance the user's exploration of the Bible. The tool aims to deliver lightning-fast searches and insightful summaries of both the Old Testament and New Testament.

WhenX is a tool for creating Semantic Alerts that allows users to create robots to monitor the web for specific information. Users can ask questions, and the tool will search the web for answers, monitoring for any changes. It is a personal project not intended for commercial use, built on open-source software and hosted on Vercel. WhenX provides a simple and efficient way to stay updated on topics of interest by setting up alerts for specific queries.

ProductHunt AI 2.0 is a no-BS product finder that makes it super easy to find super-effective products and alternatives on the go with semantic searches. It is 100% free to use and backed by Supervised AI. With ProductHunt AI 2.0, you can build agents like this with no-code, use free open-source AI models, or deploy your own language model.

Keytalk AI is a company that specializes in prompt engineering, which is the process of creating prompts that can be used to generate text, images, and other types of content using artificial intelligence (AI) models. Keytalk AI's mission is to make AI more accessible and user-friendly by providing tools and resources that make it easy for people to create and use AI-generated content. The company's flagship product is Keytalk Prompts, a library of pre-written prompts that can be used to generate content on a variety of topics. Keytalk AI also offers a range of other services, including consulting, training, and support.

Spin Rewriter AI is an article rewriter that uses artificial intelligence to generate unique, human-quality content. It is the only rewriter that uses the power of Large Language Models (LLMs) to extract the meaning of your articles on an entirely different level. This means that Spin Rewriter AI can pinpoint the meaning of every word in your article and how each word relates to every other word in its context. This allows Spin Rewriter AI to create human-quality readable articles with ZERO machine-generated footprint at a push of a button.

The website offers an AI-powered editor widget that allows users to make real-time edits directly on their website. It leverages advanced AI technology from OpenAI to streamline content editing and enhance productivity. Users can choose between GPT-3 and GPT-4 models for editing tasks. The tool also provides manual editing options for correcting errors in AI-generated content. Additionally, users can effortlessly edit multiple elements simultaneously, inspect deeper structures of webpages, and evaluate and publish content with control over what is visible to clients.

SmarterFolder is an AI-powered tool designed for MacOS that enables users to perform semantic image searches on their local drive. By utilizing AI technology, users can find photos based on descriptions of the content within the images. The tool ensures full privacy as no images are shared or stored externally, providing a secure and efficient way to organize and retrieve photos.

illumex is a generative semantic fabric platform designed to streamline the process of data and analytics interpretation and rationalization for complex enterprises. It offers augmented analytics creation, suggestive data and analytics utilization monitoring, and automated knowledge documentation to enhance agentic performance for analytics. The platform aims to solve the challenges of traditional tedious data analysis, incongruent data and metrics, and tribal knowledge of data teams.

Maimovie is an AI-powered movie and TV show search engine that helps users find content based on their specific moods or contexts. It offers an infinite number of personal recommendations based on user preferences, as well as live-updated AI curation of movie and TV show rankings trending on popular streaming services. Maimovie provides detailed information about each movie and TV show, including plot, available streaming services, ratings, cast, crew, and similar movies.

Pongo is an AI-powered tool that helps reduce hallucinations in Large Language Models (LLMs) by up to 80%. It utilizes multiple state-of-the-art semantic similarity models and a proprietary ranking algorithm to ensure accurate and relevant search results. Pongo integrates seamlessly with existing pipelines, whether using a vector database or Elasticsearch, and processes top search results to deliver refined and reliable information. Its distributed architecture ensures consistent latency, handling a wide range of requests without compromising speed. Pongo prioritizes data security, operating at runtime with zero data retention and no data leaving its secure AWS VPC.

Level AI is a provider of artificial intelligence (AI)-powered solutions for call centers. Its products include GenAI-automated Quality Assurance, Contact Center and Business Analytics, GenAI-powered VoC Insights, AgentGPT Real-Time Agent Assist, Agent Coaching, Agent Screen Recording, and Artificial Intelligence Integrations. Level AI's solutions are designed to help businesses improve customer experience, increase efficiency, and reduce costs. The company's customers include some of the world's leading customer service organizations, such as Brex and ezCater.

Wordmetrics is an AI-powered writing platform that helps content marketers get more traffic. It provides real-time guidance on writing for search, analyzes competing pages, and optimizes content for contextual relevance. Wordmetrics is cloud-based and easy to use, with a simple toolset that lets you log in and go.

Raz Official is a native sales platform that helps businesses increase engagement and drive sales by converting leads through the most engaging messaging channel. The platform offers features such as group chats, voice memos, videos, and photos, along with transparent pricing based on phone lines. It eliminates the need for A2P registration, saving time and enabling immediate messaging. With a focus on iMessage integration, Raz Official provides a seamless experience for users to communicate and collaborate effectively.

Thesify.ai is an AI essay writing tool designed to assist students in academic writing. It offers features such as writing-coach, semantic article search, pre-submission assessment, intelligent paper digest, and agile editor. The tool aims to provide AI-powered assistance, insights, and feedback to help students improve their writing skills while maintaining academic integrity.

Ultra AI is an all-in-one AI command center for products, offering features such as multi-provider AI gateway, prompts manager, semantic caching, logs & analytics, model fallbacks, and rate limiting. It is designed to help users efficiently manage and utilize AI capabilities in their products. The platform is privacy-focused, fast, and provides quick support, making it a valuable tool for businesses looking to leverage AI technology.

Trieve is an AI-first infrastructure API that offers a modern solution for search, recommendations, and RAG (Retrieve and Generate) tasks. It combines language models with tools for fine-tuning ranking and relevance, providing production-ready capabilities for building search, discovery, and RAG experiences. Trieve supports semantic vector search, full-text search using BM25 & SPLADE models, custom embedding models, hybrid search, and sub-sentence highlighting. With features like merchandising, relevance tuning, and self-hostable options, Trieve empowers companies to enhance their search capabilities and user experiences.

EnergeticAI is an open-source AI library that can be used in Node.js applications. It is optimized for serverless environments and provides fast cold-start, small module size, and pre-trained models. EnergeticAI can be used for a variety of tasks, including building recommendations, classifying text, and performing semantic search.

InternVL scales up the ViT to _**6B parameters**_ and aligns it with LLM. It is a vision-language foundation model that can perform various tasks, including: **Visual Perception** - Linear-Probe Image Classification - Semantic Segmentation - Zero-Shot Image Classification - Multilingual Zero-Shot Image Classification - Zero-Shot Video Classification **Cross-Modal Retrieval** - English Zero-Shot Image-Text Retrieval - Chinese Zero-Shot Image-Text Retrieval - Multilingual Zero-Shot Image-Text Retrieval on XTD **Multimodal Dialogue** - Zero-Shot Image Captioning - Multimodal Benchmarks with Frozen LLM - Multimodal Benchmarks with Trainable LLM - Tiny LVLM InternVL has been shown to achieve state-of-the-art results on a variety of benchmarks. For example, on the MMMU image classification benchmark, InternVL achieves a top-1 accuracy of 51.6%, which is higher than GPT-4V and Gemini Pro. On the DocVQA question answering benchmark, InternVL achieves a score of 82.2%, which is also higher than GPT-4V and Gemini Pro. InternVL is open-sourced and available on Hugging Face. It can be used for a variety of applications, including image classification, object detection, semantic segmentation, image captioning, and question answering.

The AiTLAS toolbox (Artificial Intelligence Toolbox for Earth Observation) includes state-of-the-art machine learning methods for exploratory and predictive analysis of satellite imagery as well as a repository of AI-ready Earth Observation (EO) datasets. It can be easily applied for a variety of Earth Observation tasks, such as land use and cover classification, crop type prediction, localization of specific objects (semantic segmentation), etc. The main goal of AiTLAS is to facilitate better usability and adoption of novel AI methods (and models) by EO experts, while offering easy access and standardized format of EO datasets to AI experts which allows benchmarking of various existing and novel AI methods tailored for EO data.

SimpleAICV_pytorch_training_examples is a repository that provides simple training and testing examples for various computer vision tasks such as image classification, object detection, semantic segmentation, instance segmentation, knowledge distillation, contrastive learning, masked image modeling, OCR text detection, OCR text recognition, human matting, salient object detection, interactive segmentation, image inpainting, and diffusion model tasks. The repository includes support for multiple datasets and networks, along with instructions on how to prepare datasets, train and test models, and use gradio demos. It also offers pretrained models and experiment records for download from huggingface or Baidu-Netdisk. The repository requires specific environments and package installations to run effectively.

This repository provides advanced AI explainability for PyTorch, offering state-of-the-art methods for Explainable AI in computer vision. It includes a comprehensive collection of Pixel Attribution methods for various tasks like Classification, Object Detection, Semantic Segmentation, and more. The package supports high performance with full batch image support and includes metrics for evaluating and tuning explanations. Users can visualize and interpret model predictions, making it suitable for both production and model development scenarios.

cellseg-models.pytorch is a Python library built upon PyTorch for 2D cell/nuclei instance segmentation models. It provides multi-task encoder-decoder architectures and post-processing methods for segmenting cell/nuclei instances. The library offers high-level API to define segmentation models, open-source datasets for training, flexibility to modify model components, sliding window inference, multi-GPU inference, benchmarking utilities, regularization techniques, and example notebooks for training and finetuning models with different backbones.

MOOSE 2.0 is a leaner, meaner, and stronger tool for 3D medical image segmentation. It is built on the principles of data-centric AI and offers a wide range of segmentation models for both clinical and preclinical settings. MOOSE 2.0 is also versatile, allowing users to use it as a command-line tool for batch processing or as a library package for individual processing in Python projects. With its improved speed, accuracy, and flexibility, MOOSE 2.0 is the go-to tool for segmentation tasks.

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.

This repository contains a collection of papers and code for the CVPR 2024 conference. The papers cover a wide range of topics in computer vision, including object detection, image segmentation, image generation, and video analysis. The code provides implementations of the algorithms described in the papers, making it easy for researchers and practitioners to reproduce the results and build upon the work of others. The repository is maintained by a team of researchers at the University of California, Berkeley.

The `sec-parser` project simplifies extracting meaningful information from SEC EDGAR HTML documents by organizing them into semantic elements and a tree structure. It helps in parsing SEC filings for financial and regulatory analysis, analytics and data science, AI and machine learning, causal AI, and large language models. The tool is especially beneficial for AI, ML, and LLM applications by streamlining data pre-processing and feature extraction.

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.

Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to increase the quality of trained models. The purpose of image augmentation is to create new training samples from the existing data.

Nitrain is a framework for medical imaging AI that provides tools for sampling and augmenting medical images, training models on medical imaging datasets, and visualizing model results in a medical imaging context. It supports using pytorch, keras, and tensorflow.

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.

Hailo TAPPAS is a set of full application examples that implement pipeline elements and pre-trained AI tasks. It demonstrates Hailo's system integration scenarios on predefined systems, aiming to accelerate time to market, simplify integration with Hailo's runtime SW stack, and provide a starting point for customers to fine-tune their applications. The tool supports both Hailo-15 and Hailo-8, offering various example applications optimized for different common hosts. TAPPAS includes pipelines for single network, two network, and multi-stream processing, as well as high-resolution processing via tiling. It also provides example use case pipelines like License Plate Recognition and Multi-Person Multi-Camera Tracking. The tool is regularly updated with new features, bug fixes, and platform support.

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.

MindNLP is an open-source NLP library based on MindSpore. It provides a platform for solving natural language processing tasks, containing many common approaches in NLP. It can help researchers and developers to construct and train models more conveniently and rapidly. Key features of MindNLP include: * Comprehensive data processing: Several classical NLP datasets are packaged into a friendly module for easy use, such as Multi30k, SQuAD, CoNLL, etc. * Friendly NLP model toolset: MindNLP provides various configurable components. It is friendly to customize models using MindNLP. * Easy-to-use engine: MindNLP simplified complicated training process in MindSpore. It supports Trainer and Evaluator interfaces to train and evaluate models easily. MindNLP supports a wide range of NLP tasks, including: * Language modeling * Machine translation * Question answering * Sentiment analysis * Sequence labeling * Summarization MindNLP also supports industry-leading Large Language Models (LLMs), including Llama, GLM, RWKV, etc. For support related to large language models, including pre-training, fine-tuning, and inference demo examples, you can find them in the "llm" directory. To install MindNLP, you can either install it from Pypi, download the daily build wheel, or install it from source. The installation instructions are provided in the documentation. MindNLP is released under the Apache 2.0 license. If you find this project useful in your research, please consider citing the following paper: @misc{mindnlp2022, title={{MindNLP}: a MindSpore NLP library}, author={MindNLP Contributors}, howpublished = {\url{https://github.com/mindlab-ai/mindnlp}}, year={2022} }

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

Awesome Mamba is a curated collection of groundbreaking research papers and articles on Mamba Architecture, a pioneering framework in deep learning known for its selective state spaces and efficiency in processing complex data structures. The repository offers a comprehensive exploration of Mamba architecture through categorized research papers covering various domains like visual recognition, speech processing, remote sensing, video processing, activity recognition, image enhancement, medical imaging, reinforcement learning, natural language processing, 3D recognition, multi-modal understanding, time series analysis, graph neural networks, point cloud analysis, and tabular data handling.

RK3588 is a flagship 8K SoC chip by Rockchip, integrating Cortex-A76 and Cortex-A55 cores with NEON coprocessor for 8K video codec. This repository curates resources for developing with RK3588, including official resources, RKNN models, projects, development boards, documentation, tools, and sample code.

Analyse & visualise semantic networks entities and attributes for content creation.

Guiding on Semantic SEO, from understanding core concepts to applying advanced strategies.

BIAN v12.0

Typescript-based expert in NextJS, Semantic-ui

Aids in SPARQL queries for Semantic-Art's Gist Ontology

Talk to Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning (LFG)

Helps you build logic security into your GPTs custom instructions. Documentation: https://github.com/infotrix/SSLLMs---Semantic-Secuirty-for-LLM-GPTs

Experto en lógica semántica SEO y resolución de problemas, formado por Pau Segui.

Esperto di SEO in analisi semantica, keyword research e messy middle funnel che interagisce con docs e sheets

Expert in schema.org, guiding precise use of 'additionalType'.

A linguistic explorer that delves into the depths of words and phrases, revealing their richest meanings and most resonant synonyms, closely aligned with their original intent.