Best AI Vision Models for Nonprofit

Your software can see objects in video and images. A few dozen images can be used to train a computer vision model. This takes less than 24 hours. We support innovators just like you in applying computer vision. Upload files via API or manually, including images, annotations, videos, and audio. There are many annotation formats that we support and it is easy to add training data as you gather it. Roboflow Annotate was designed to make labeling quick and easy. Your team can quickly annotate hundreds upon images in a matter of minutes. You can assess the quality of your data and prepare them for training. Use transformation tools to create new training data. See what configurations result in better model performance. All your experiments can be managed from one central location. You can quickly annotate images right from your browser. Your model can be deployed to the cloud, the edge or the browser. Predict where you need them, in half the time.

Available in more than 20 programming languages, including Python, JavaScript and TypeScript, Ruby, TypeScript, Go, Ruby and many others. BLACKBOX AI code search was created so that developers could find the best code fragments to use when building amazing products. Integrations with IDEs include VS Code and Github Codespaces. Jupyter Notebook, Paperspace, and many more. C#, Java, C++, C# and SQL, PHP, Go and TypeScript are just a few of the languages that can be used to search code in Python, Java and C++. It is not necessary to leave your coding environment in order to search for a specific function. Blackbox allows you to select the code from any video and then simply copy it into your text editor. Blackbox supports all programming languages and preserves the correct indentation. The Pro plan allows you to copy text from over 200 languages and all programming languages.

GPT-4o (o for "omni") is an important step towards a more natural interaction between humans and computers. It accepts any combination as input, including text, audio and image, and can generate any combination of outputs, including text, audio and image. It can respond to audio in as little as 228 milliseconds with an average of 325 milliseconds. This is similar to the human response time in a conversation (opens in new window). It is as fast and cheaper than GPT-4 Turbo on text in English or code. However, it has a significant improvement in text in non-English language. GPT-4o performs better than existing models at audio and vision understanding.

Create AI applications that are market-ready and cutting-edge with customizable APIs and models. Studio, SDKs and APIs can be used to quickly integrate generative AI into production workloads. Build AI apps that are powered by foundation models from OpenAI Meta and Microsoft to gain a competitive advantage. With Azure Security, responsible AI tools, and built-in AI, you can detect and mitigate harmful usage. Create your own copilot applications and generative AI with the latest language and vision models. Search for the most relevant information using hybrid, vector and keyword search. Monitor images and text to detect offensive content. Translate documents and text in more than 100 different languages.

A small model with superior textual Intelligence and multimodal reasoning. GPT-4o Mini's low cost and low latency enable a wide range of tasks, including applications that chain or paralelize multiple model calls (e.g. calling multiple APIs), send a large amount of context to the models (e.g. full code base or history of conversations), or interact with clients through real-time, fast text responses (e.g. customer support chatbots). GPT-4o Mini supports text and vision today in the API. In the future, it will support text, image and video inputs and outputs. The model supports up to 16K outputs tokens per request and has knowledge until October 2023. It has a context of 128K tokens. The improved tokenizer shared by GPT-4o makes it easier to handle non-English text.

GPT-4 with Vision (GPT-4V), our latest capability, allows users to instruct GPT-4 on how to analyze images input by the user. Some researchers and developers of artificial intelligence consider the incorporation of additional modalities, such as image inputs, into large language models. Multimodal LLMs can be used to expand the impact of existing language-only systems by providing them with novel interfaces, capabilities and experiences. In this system card we analyze the GPT-4V safety properties. We have built on the safety work for GPT-4V and here we go deeper into the evaluations and preparations for image inputs.

Fully managed ML tools allow you to build, deploy and scale machine-learning (ML) models quickly, for any use case. Vertex AI Workbench is natively integrated with BigQuery Dataproc and Spark. You can use BigQuery to create and execute machine-learning models in BigQuery by using standard SQL queries and spreadsheets or you can export datasets directly from BigQuery into Vertex AI Workbench to run your models there. Vertex Data Labeling can be used to create highly accurate labels for data collection. Vertex AI Agent Builder empowers developers to design and deploy advanced generative AI applications for enterprise use. It supports both no-code and code-driven development, enabling users to create AI agents through natural language prompts or by integrating with frameworks like LangChain and LlamaIndex.

Qwen2.5, an advanced multimodal AI system, is designed to provide highly accurate responses that are context-aware across a variety of applications. It builds on its predecessors' capabilities, integrating cutting edge natural language understanding, enhanced reasoning, creativity and multimodal processing. Qwen2.5 is able to analyze and generate text as well as interpret images and interact with complex data in real-time. It is highly adaptable and excels at personalized assistance, data analytics, creative content creation, and academic research. This makes it a versatile tool that can be used by professionals and everyday users. Its user-centric approach emphasizes transparency, efficiency and alignment with ethical AI.

Fullmoon, an open-source, free application, allows users to interact directly with large language models on their devices. This ensures privacy and offline accessibility. It is optimized for Apple silicon and works seamlessly across iOS, iPadOS macOS, visionOS platforms. Users can customize the app with themes, fonts and system prompts. It also integrates with Apple Shortcuts to enhance functionality. Fullmoon supports models like Llama-3.2-1B-Instruct-4bit and Llama-3.2-3B-Instruct-4bit, facilitating efficient on-device AI interactions without the need for an internet connection.

Falcon 2 11B is a cutting-edge open-source AI model, designed for multilingual and multimodal tasks, and the only one featuring vision-to-language capabilities. It outperforms Meta’s Llama 3 8B and rivals Google’s Gemma 7B, as verified by the Hugging Face Leaderboard. The next step in its evolution includes integrating a 'Mixture of Experts' framework to further elevate its performance and expand its capabilities.

Qwen2.5-VL is an advanced vision-language model in the Qwen series, offering improved visual comprehension and reasoning over its predecessor, Qwen2-VL. It can accurately interpret a wide range of visual elements, including text, charts, icons, and layouts, making it highly effective for complex image and document analysis. Acting as an intelligent visual agent, the model can dynamically interact with tools, analyze extended video content over an hour long, and identify key segments with precision. It also excels in object localization, generating bounding boxes or points with structured JSON outputs for various attributes. Additionally, Qwen2.5-VL supports structured data extraction from documents such as invoices, forms, and tables, benefiting industries like finance and commerce. Available in base and instruct versions across 3B, 7B, and 72B model sizes, it is accessible on platforms like Hugging Face and ModelScope for seamless integration.

Mistral AI announced a number of key updates on September 17, 2024 to improve the accessibility and performance. They introduced a free version of "La Plateforme", their serverless platform, which allows developers to experiment with and prototype Mistral models at no cost. Mistral AI has also reduced the prices of their entire model line, including a 50% discount for Mistral Nemo, and an 80% discount for Mistral Small and Codestral. This makes advanced AI more affordable for users. The company also released Mistral Small v24.09 - a 22-billion parameter model that offers a balance between efficiency and performance, and is suitable for tasks such as translation, summarization and sentiment analysis. Pixtral 12B is a model with image understanding abilities that can be used to analyze and caption pictures without compromising text performance.

SmolVLM-Instruct is an advanced multimodal AI model that excels at integrating both text and image inputs for tasks like image captioning, visual Q&A, and generating narratives based on visual content. Optimized for smaller, more efficient performance, it uses SmolLM2 for text decoding and SigLIP for image encoding. This makes it suitable for on-device applications or other environments with limited resources while still delivering high-quality results. SmolVLM-Instruct is designed to be fine-tuned for various tasks, enabling businesses to build more interactive and intelligent applications that require the fusion of visual and textual data.

You can train your own models, access pre-trained computer vision models, and templates for creating AI apps. You can start creating your own dataset to detect objects by adding images of the object. Each dataset can contain up to 5000 images. Images are automatically added to your dataset and pushed into training. You will be notified once the model has finished training. To use your model for detection, you can simply download it. For quick coding, you can also add your model to one of our pre-existing templates. Our mobile app, which is available for both Android and IOS, uses the power of computer vision in order to assist people with complete blindness in their daily lives. It can alert you to dangerous objects and signs, recognize common objects, recognize text as well currencies, and understand basic scenarios using deep learning.

Lodestar is a complete solution for creating computer vision models from video data. The world's first active learning data annotation platform allows you to label hours of video and speed up the creation of high-quality datasets and computer vision models. Automated data preparation makes it easy to drag and drop 10 hours worth of video into one project. Multiple video formats are supported and no data curation is required. Annotators and data scientists can collaborate to create a functional object detection model within an hour by using continuous model training and a shared managed dataset. Every plan comes with unlimited labels.

AskUI is an advanced automation platform that enables AI agents to visually interpret and interact with any digital interface, making it possible to automate workflows across multiple operating systems, including Windows, macOS, Linux, and mobile devices. Using its proprietary PTA-1 prompt-to-action model, AskUI allows for AI-driven execution of tasks without requiring modifications like jailbreaking. The platform is ideal for automating UI interactions, visual testing, and data-driven processes, streamlining operations for developers and enterprises alike. It seamlessly integrates with popular tools like Jira, Jenkins, GitLab, and Docker to enhance efficiency and workflow automation. Companies leveraging AskUI have reported significant productivity gains, with some achieving over 90% improvements in test automation and internal processes.

Create a custom computer vision model in minutes. AI Custom Vision is part of Azure AI services and allows you to customize and embed the latest computer vision image analysis in specific domains. Create frictionless customer experiences. Optimize manufacturing processes. Accelerate digital marketing campaigns. No machine learning knowledge is required. Set your model to recognize a specific object for your application. Easy to build your image identifier using the simple interface. Upload and label a few images to start training your computer vision models. The model will test itself and improve its precision as you add more images. Use customizable, built-in retail, manufacturing, or food models to speed up development. Minsur, the world's largest mine of tin, uses AI Custom Vision to achieve sustainable mining. You can rely on enterprise-grade privacy and security for your data.

Qwen2-VL, the latest version in the Qwen model family of vision language models, is based on Qwen2. Qwen2-VL is a newer version of Qwen-VL that has: SoTA understanding of images with different resolutions & ratios: Qwen2-VL reaches state-of-the art performance on visual understanding benchmarks including MathVista DocVQA RealWorldQA MTVQA etc. Understanding videos over 20 min: Qwen2-VL is able to understand videos longer than 20 minutes, allowing for high-quality video-based questions, dialogs, content creation, and more. Agent that can control your mobiles, robotics, etc. Qwen2-VL, with its complex reasoning and decision-making abilities, can be integrated into devices such as mobile phones, robots and other devices for automatic operation using visual environment and text instruction. Multilingual Support - To serve users worldwide, Qwen2-VL supports texts in other languages within images, besides English or Chinese.

Pixtral Large is Mistral AI’s latest open-weight multimodal model, featuring a powerful 124-billion-parameter architecture. It combines a 123-billion-parameter multimodal decoder with a 1-billion-parameter vision encoder, allowing it to excel at interpreting documents, charts, and natural images while maintaining top-tier text comprehension. With a 128,000-token context window, it can process up to 30 high-resolution images simultaneously. The model has achieved cutting-edge results on benchmarks like MathVista, DocVQA, and VQAv2, outperforming competitors such as GPT-4o and Gemini-1.5 Pro. Available under the Mistral Research License for non-commercial use and the Mistral Commercial License for enterprise applications, Pixtral Large is designed for advanced AI-powered understanding.

Palmyra is an enterprise-ready suite of Large Language Models. These models are excellent at tasks like image analysis, question answering, and supporting over 30 languages. They can be fine-tuned for industries such as healthcare and finance. Palmyra models are notable for their top rankings in benchmarks such as Stanford HELM and PubMedQA. Palmyra Fin is the first model that passed the CFA Level III examination. Writer protects client data by not using it to train or modify models. They have a zero-data retention policy. Palmyra includes specialized models, such as Palmyra X 004, which has tool-calling abilities; Palmyra Med for healthcare; Palmyra Fin for finance; and Palmyra Vision for advanced image and video processing. These models are available via Writer's full stack generative AI platform which integrates graph based Retrieval augmented Generation (RAG).

LLaVA is a multimodal model that combines a Vicuna language model with a vision encoder to facilitate comprehensive visual-language understanding. LLaVA's chat capabilities are impressive, emulating multimodal functionality of models such as GPT-4. LLaVA 1.5 has achieved the best performance in 11 benchmarks using publicly available data. It completed training on a single 8A100 node in about one day, beating methods that rely upon billion-scale datasets. The development of LLaVA involved the creation of a multimodal instruction-following dataset, generated using language-only GPT-4. This dataset comprises 158,000 unique language-image instruction-following samples, including conversations, detailed descriptions, and complex reasoning tasks. This data has been crucial in training LLaVA for a wide range of visual and linguistic tasks.

IBM Maximo Visual Inspection gives your quality control and inspection team the power of AI computer vision capabilities. It is an intuitive toolkit for labelling, training and deploying artificial vision models. You can quickly and easily deploy your model by using our drag-and-drop visual user interface. Or, you can import a model. IBM Maximo Visual Inspection allows you to create your own detect-and-correct solution using self learning machine algorithms. Watch the video below to see how easy it is automate your inspections with visual inspection tools.

Ray2 is an advanced video generative model that can create realistic visuals and natural, coherent movement. It can be trained to understand text instructions, and it can also take video and images as input. Ray2 has advanced capabilities because it was trained on Luma’s new multimodal architecture, which is 10x more powerful than Ray1. Ray2 is the first of a new generation video models that can produce fast, coherent motions, ultra-realistic detail, and logical sequences of events. This increases the number of successful generations and makes Ray2 videos more production-ready. Ray2 offers text-to video generation, and will soon add image-to, video-to, and editing features. Ray2 offers a new level of motion accuracy. Transform your vision into a smooth, cinematic and jaw-dropping reality. Visually tell your story using stunning cinematic visuals. Ray2 allows you to create stunning scenes with precise camera movement.

Florence-2-large, a Microsoft vision foundation model, is capable of handling many vision and vision-language-related tasks, including captioning, object detection and segmentation, as well as OCR. It is built with a sequence to sequence architecture and uses the FLD-5B dataset, which contains over 5 billion annotations on 126 million images, to master multi-tasking learning. Florence-2-large is a powerful tool for both zero-shots and fine-tuned settings. It produces high-quality results even with minimal training. The model can perform tasks such as detailed captioning and object detection. It also supports dense region captioning and can process images using text prompts. It is a powerful tool for AI-powered visual tasks because it can handle a wide range of vision-related tasks using prompt-based approaches. The model is available with pre-trainedweights on Hugging Face, allowing users to quickly start with image processing and task completion.

Our fully managed solution makes it easy to create training datasets for computer-vision models. Data labeling is a key factor in creating effective deep learning models. We aim to be the industry's most trusted data labeling platform, helping companies fully take advantage of AI's potential. You can organize your media using discrete categories. You can identify items of interest using one or more bounding boxes. Similar to bounding boxes but with more precision. You can annotate objects with precise width, depth, height. Each pixel in an image should be classified. Each point in an image should be marked. Annotate straight lines within an image. Measure, yaw and pitch the item of interest. Annotate timestamps in audio and video content. Annotate lines that are not defined in an image.