Best AI tools for< Train Image Recognition Models >

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.

Imagga is a leading provider of image recognition solutions for developers and businesses. Its API empowers intelligent apps with customizable machine learning technology. Imagga's solutions include tagging, categorization, cropping, color extraction, visual search, facial recognition, custom training, and content moderation. These solutions are used by over 30K startups, developers, and students, and trusted by over 200 business customers in more than 82 countries worldwide.

Image Bear AI is an advanced image recognition tool that utilizes artificial intelligence to analyze and identify objects within images. The application is designed to assist users in various industries such as e-commerce, security, and healthcare by providing accurate and efficient image analysis capabilities. With its cutting-edge technology, Image Bear AI offers a user-friendly interface and fast processing speeds, making it a valuable tool for businesses looking to streamline their image recognition processes.

PyAI is an advanced AI tool designed for developers and data scientists to streamline their workflow and enhance productivity. It offers a wide range of AI capabilities, including machine learning algorithms, natural language processing, computer vision, and more. With PyAI, users can easily build, train, and deploy AI models for various applications, such as predictive analytics, image recognition, and text classification. The tool provides a user-friendly interface and comprehensive documentation to support users at every stage of their AI projects.

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.

Datagen is a platform that provides synthetic data for computer vision. Synthetic data is artificially generated data that can be used to train machine learning models. Datagen's data is generated using a variety of techniques, including 3D modeling, computer graphics, and machine learning. The company's data is used by a variety of industries, including automotive, security, smart office, fitness, cosmetics, and facial applications.

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.

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.

Vize.ai is a custom image recognition API provided by Ximilar, a leading company in Visual AI and Search. The tool offers powerful artificial intelligence capabilities with high accuracy using deep learning algorithms. It allows users to easily set up and implement cutting-edge vision automation without any development costs. Vize.ai enables users to train custom neural networks to recognize specific images and provides a scalable solution with continuous improvements in machine learning algorithms. The tool features an intuitive interface that requires no machine learning or coding knowledge, making it accessible for a wide range of users across industries.

Neural4D is an AI tool designed to provide advanced neural network solutions. It offers a range of features for deep learning applications, including image recognition, natural language processing, and predictive analytics. With Neural4D, users can build and train complex neural networks to solve various real-world problems. The tool is user-friendly and suitable for both beginners and experienced AI practitioners.

FotoExamen is an AI-powered platform designed to help students solve exams and tasks quickly and accurately by uploading a photo. It offers features such as resolving exams with AI, creating mind maps, chatting with AI tutors, and more. FotoExamen provides personalized tools and visual aids to enhance the study experience, catering to students, teachers, educational institutions, parents, tutors, and researchers. The platform ensures data security, multilingual support, 24/7 availability, and secure payment processing. FotoExamen stands out from competitors like ChatGPT by offering specialized AI for exam analysis, image recognition, competitive pricing, and additional learning tools.

TrainEngine.ai is a powerful AI-powered image generation tool that allows users to create stunning, unique images from text prompts. With its advanced algorithms and vast dataset, TrainEngine.ai can generate images in a wide range of styles, from realistic to abstract, and in various formats, including photos, paintings, and illustrations. The platform is easy to use, making it accessible to both professional artists and hobbyists alike. TrainEngine.ai offers a range of features, including the ability to fine-tune models, generate unlimited AI assets, and access trending models. It also provides a marketplace where users can buy and sell AI-generated images.

AI Image Generator Free is a powerful online tool that allows users to create and edit images using the capabilities of artificial intelligence. Users can easily generate images from text, edit photos with words, expand pictures beyond their borders, train custom AI models, and much more. The tool offers a variety of features to enhance creativity and streamline image creation processes.

Tess is the first AI image generator that empowers artists to own their style by creating properly-licensed images. It offers a world-class image editor designed for AI, allowing users to generate art in a consistent visual style. Tess enables artists to create models, edit and customize their generations, and discover how AI can enhance their artistic style. With Tess, users can access copyright-safe generations created by real artists, ensuring ethical AI art practices.

Helix AI is a private GenAI platform that enables users to build AI applications using open source models. The platform offers tools for RAG (Retrieval-Augmented Generation) and fine-tuning, allowing deployment on-premises or in a Virtual Private Cloud (VPC). Users can access curated models, utilize Helix API tools to connect internal and external APIs, embed Helix Assistants into websites/apps for chatbot functionality, write AI application logic in natural language, and benefit from the innovative RAG system for Q&A generation. Additionally, users can fine-tune models for domain-specific needs and deploy securely on Kubernetes or Docker in any cloud environment. Helix Cloud offers free and premium tiers with GPU priority, catering to individuals, students, educators, and companies of varying sizes.

Stable Diffusion is an AI art generation tool that allows users to create high-quality images from text descriptions. It offers a user-friendly platform for both beginners and experts to explore AI art creation without deep technical knowledge. The tool excels in producing complex, detailed, and customizable images, making it ideal for artists, designers, and anyone looking to integrate AI into their creative process. Stable Diffusion provides unprecedented creative freedom through features like image generation, inpainting, outpainting, and text-guided image-to-image translation.

ProJourney AI is a generative AI tool designed for designers and creators. It offers private AI image generation, enabling users to create high-quality images without sharing them publicly. Users can create amazing images starting with a text prompt or by uploading existing images to train the AI model. ProJourney simplifies AI image creation by providing access to Midjourney's generator without Discord, making the process easy and efficient.

Facet is a cutting-edge generative imagery tool that helps creative professionals focus on what matters. It provides creative assistance without trading off artistic control. Facet helps overcome time and resource constraints that prevent trying out ideas. It offers an intuitive image generation experience with more than just text prompts, including image references, automatic prompt variations, and even custom models trained on the user's exact aesthetic. Facet allows users to train a custom model using their own images in minutes, generating endless assets in their exact vision. Users can add image references to any prompt, instantly getting images that adhere to their subject or style. Facet provides a collaborative canvas for users to riff with teammates and build off of each other's prompts and ideas.

CrazyHorseAI is an AI tool that offers an API for users to enhance and customize the appearance and personality traits of an AI girl through features like changing clothes, hair, body, pose, and background. The tool provides functionalities such as natural language processing, emotional intelligence, and adaptive learning capabilities to create immersive and engaging experiences.

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 Intel® AI Reference Models repository contains links to pre-trained models, sample scripts, best practices, and tutorials for popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors and Intel® Data Center GPUs. The purpose is to quickly replicate complete software environments showcasing the AI capabilities of Intel platforms. It includes optimizations for popular deep learning frameworks like TensorFlow and PyTorch, with additional plugins/extensions for improved performance. The repository is licensed under Apache License Version 2.0.

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.

Intel® Neural Compressor is an open-source Python library that supports popular model compression techniques such as quantization, pruning (sparsity), distillation, and neural architecture search on mainstream frameworks such as TensorFlow, PyTorch, ONNX Runtime, and MXNet. It provides key features, typical examples, and open collaborations, including support for a wide range of Intel hardware, validation of popular LLMs, and collaboration with cloud marketplaces, software platforms, and open AI ecosystems.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

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

Efficient Foundation Model Survey is a comprehensive analysis of resource-efficient large language models (LLMs) and multimodal foundation models. The survey covers algorithmic and systemic innovations to support the growth of large models in a scalable and environmentally sustainable way. It explores cutting-edge model architectures, training/serving algorithms, and practical system designs. The goal is to provide insights on tackling resource challenges posed by large foundation models and inspire future breakthroughs in the field.

SenseVoice is a speech foundation model focusing on high-accuracy multilingual speech recognition, speech emotion recognition, and audio event detection. Trained with over 400,000 hours of data, it supports more than 50 languages and excels in emotion recognition and sound event detection. The model offers efficient inference with low latency and convenient finetuning scripts. It can be deployed for service with support for multiple client-side languages. SenseVoice-Small model is open-sourced and provides capabilities for Mandarin, Cantonese, English, Japanese, and Korean. The tool also includes features for natural speech generation and fundamental speech recognition tasks.

LangTest is a comprehensive evaluation library for custom LLM and NLP models. It aims to deliver safe and effective language models by providing tools to test model quality, augment training data, and support popular NLP frameworks. LangTest comes with benchmark datasets to challenge and enhance language models, ensuring peak performance in various linguistic tasks. The tool offers more than 60 distinct types of tests with just one line of code, covering aspects like robustness, bias, representation, fairness, and accuracy. It supports testing LLMS for question answering, toxicity, clinical tests, legal support, factuality, sycophancy, and summarization.

Promptbook is a library designed to build responsible, controlled, and transparent applications on top of large language models (LLMs). It helps users overcome limitations of LLMs like hallucinations, off-topic responses, and poor quality output by offering features such as fine-tuning models, prompt-engineering, and orchestrating multiple prompts in a pipeline. The library separates concerns, establishes a common format for prompt business logic, and handles low-level details like model selection and context size. It also provides tools for pipeline execution, caching, fine-tuning, anomaly detection, and versioning. Promptbook supports advanced techniques like Retrieval-Augmented Generation (RAG) and knowledge utilization to enhance output quality.

This repository is a compilation of excellent ML and DL tutorials created by various individuals and organizations. It covers a wide range of topics, including machine learning fundamentals, deep learning, computer vision, natural language processing, reinforcement learning, and more. The resources are organized into categories, making it easy to find the information you need. Whether you're a beginner or an experienced practitioner, you're sure to find something valuable in this repository.

The Intel® AI Reference Models repository contains links to pre-trained models, sample scripts, best practices, and tutorials for popular open-source machine learning models optimized by Intel to run on Intel® Xeon® Scalable processors and Intel® Data Center GPUs. It aims to replicate the best-known performance of target model/dataset combinations in optimally-configured hardware environments. The repository will be deprecated upon the publication of v3.2.0 and will no longer be maintained or published.

The 100 Days in AI repository provides a comprehensive roadmap for individuals to learn Artificial Intelligence over a period of 100 days. It covers topics ranging from basic programming in Python to advanced concepts in AI, including machine learning, deep learning, and specialized AI topics. The repository includes daily tasks, resources, and exercises to ensure a structured learning experience. By following this roadmap, users can gain a solid understanding of AI and be prepared to work on real-world AI projects.

NeMo Framework is a generative AI framework built for researchers and pytorch developers working on large language models (LLMs), multimodal models (MM), automatic speech recognition (ASR), and text-to-speech synthesis (TTS). The primary objective of NeMo is to provide a scalable framework for researchers and developers from industry and academia to more easily implement and design new generative AI models by being able to leverage existing code and pretrained models.

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Job interview coach for product designers. Train interviews and say stop when you need a feedback. You got this!!

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