Best AI tools for< Image Classification >

RunwayML Experiments is a platform that allows users to create and share machine learning models. It provides a variety of tools and resources to help users get started with machine learning, including a library of pre-trained models, a visual programming interface, and a community of experts. RunwayML Experiments is used by a variety of people, including researchers, students, and hobbyists.

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.

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.

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.

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.

Airtrain is a no-code compute platform for Large Language Models (LLMs). It provides a user-friendly interface for fine-tuning, evaluating, and deploying custom AI models. Airtrain also offers a marketplace of pre-trained models that can be used for a variety of tasks, such as text generation, translation, and question answering.

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.

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.

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.

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.

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.

Garden of AI is a comprehensive AI-powered platform that provides a wide range of tools and resources to help users explore, learn, and apply AI in their daily lives and work. With a vast collection of AI models, tutorials, datasets, and community forums, Garden of AI empowers users to stay up-to-date with the latest AI advancements and leverage its capabilities to solve real-world problems.

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.

The Computer Vision Foundation (CVF) is a non-profit organization dedicated to advancing the field of computer vision. CVF organizes several conferences and workshops each year, including the International Conference on Computer Vision (ICCV), the Conference on Computer Vision and Pattern Recognition (CVPR), and the Winter Conference on Applications of Computer Vision (WACV). CVF also publishes the International Journal of Computer Vision (IJCV) and the Computer Vision and Image Understanding (CVIU) journal. The CVF Open Access website provides access to the full text of all CVF-sponsored conference papers. These papers are available for free download in PDF format. The CVF Open Access website also includes links to the arXiv versions of the papers, where available.

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.

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.

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.

Anduril Industries is a defense technology company that develops autonomous systems for land, sea, and air. The company's products include the Lattice operating system, which powers a family of autonomous systems that provide integrated, persistent awareness and security. Anduril also develops counter-UAS, counter-intrusion, and maritime counter-intrusion systems. The company's mission is to transform defense capabilities with advanced technology.

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.

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.

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.

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.

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.

MediaPipe-rs is a Rust library designed for MediaPipe tasks on WasmEdge WASI-NN. It offers easy-to-use low-code APIs similar to mediapipe-python, with low overhead and flexibility for custom media input. The library supports various tasks like object detection, image classification, gesture recognition, and more, including TfLite models, TF Hub models, and custom models. Users can create task instances, run sessions for pre-processing, inference, and post-processing, and speed up processing by reusing sessions. The library also provides support for audio tasks using audio data from symphonia, ffmpeg, or raw audio. Users can choose between CPU, GPU, or TPU devices for processing.

MLP Mixer - Pytorch is an all-MLP solution for vision tasks, developed by Google AI, implemented in Pytorch. It provides an architecture that does not require convolutions or attention mechanisms, offering an alternative approach for image and video processing. The tool is designed to handle tasks related to image classification and video recognition, utilizing multi-layer perceptrons (MLPs) for feature extraction and classification. Users can easily install the tool using pip and integrate it into their Pytorch projects to experiment with MLP-based vision models.

The `xaitk-saliency` package is an open source Explainable AI (XAI) framework for visual saliency algorithm interfaces and implementations, designed for analytics and autonomy applications. It provides saliency algorithms for various image understanding tasks such as image classification, image similarity, object detection, and reinforcement learning. The toolkit targets data scientists and developers who aim to incorporate visual saliency explanations into their workflow or product, offering both direct accessibility for experimentation and modular integration into systems and applications through Strategy and Adapter patterns. The package includes documentation, examples, and a demonstration tool for visual saliency generation in a user-interface.

The 'mslearn-ai-vision' repository contains lab files for Azure AI Vision modules. It provides hands-on exercises and resources for learning about AI vision capabilities on the Azure platform. The labs cover topics such as image recognition, object detection, and image classification using Azure's AI services. By following the lab exercises, users can gain practical experience in building and deploying AI vision solutions in the cloud.

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.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

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.

Husky is a research-focused programming language designed for next-generation computing. It aims to provide a powerful and ergonomic development experience for various tasks, including system level programming, web/native frontend development, parser/compiler tasks, game development, formal verification, machine learning, and more. With a strong type system and support for human-in-the-loop programming, Husky enables users to tackle complex tasks such as explainable image classification, natural language processing, and reinforcement learning. The language prioritizes debugging, visualization, and human-computer interaction, offering agile compilation and evaluation, multiparadigm support, and a commitment to a good ecosystem.

A high-performance TensorFlow Lite library for React Native that utilizes JSI for power, zero-copy ArrayBuffers for efficiency, and low-level C/C++ TensorFlow Lite core API for direct memory access. It supports swapping out TensorFlow Models at runtime and GPU-accelerated delegates like CoreML/Metal/OpenGL. Easy VisionCamera integration allows for seamless usage. Users can load TensorFlow Lite models, interpret input and output data, and utilize GPU Delegates for faster computation. The library is suitable for real-time object detection, image classification, and other machine learning tasks in React Native applications.

nncase is a neural network compiler for AI accelerators that supports multiple inputs and outputs, static memory allocation, operators fusion and optimizations, float and quantized uint8 inference, post quantization from float model with calibration dataset, and flat model with zero copy loading. It can be installed via pip and supports TFLite, Caffe, and ONNX ops. Users can compile nncase from source using Ninja or make. The tool is suitable for tasks like image classification, object detection, image segmentation, pose estimation, and more.

AI-Competition-Collections is a repository that collects and curates various experiences and tips from AI competitions. It includes posts on competition experiences in computer vision, NLP, speech, and other AI-related fields. The repository aims to provide valuable insights and techniques for individuals participating in AI competitions, covering topics such as image classification, object detection, OCR, adversarial attacks, and more.

ai-collective-tools is an open-source community dedicated to creating a comprehensive collection of AI tools for developers, researchers, and enthusiasts. The repository provides a curated selection of AI tools and resources across various categories such as 3D, Agriculture, Art, Audio Editing, Avatars, Chatbots, Code Assistant, Cooking, Copywriting, Crypto, Customer Support, Dating, Design Assistant, Design Generator, Developer, E-Commerce, Education, Email Assistant, Experiments, Fashion, Finance, Fitness, Fun Tools, Gaming, General Writing, Gift Ideas, HealthCare, Human Resources, Image Classification, Image Editing, Image Generator, Interior Designing, Legal Assistant, Logo Generator, Low Code, Models, Music, Paraphraser, Personal Assistant, Presentations, Productivity, Prompt Generator, Psychology, Real Estate, Religion, Research, Resume, Sales, Search Engine, SEO, Shopping, Social Media, Spreadsheets, SQL, Startup Tools, Story Teller, Summarizer, Testing, Text to Speech, Text to Image, Transcriber, Travel, Video Editing, Video Generator, Weather, Writing Generator, and Other Resources.

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.

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.

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.

Describes images with bilingual titles/keywords.

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

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

I clarify and create positive blog images with a friendly tone, ensuring any text is in English.

Your Expert on the Official 2023 Golf Rules: Simply describe or upload an image of your play scenario, and receive precise, reliable guidance on the applicable rules. Perfect for players and enthusiasts seeking accurate and instant rule clarifications

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

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