Identifies a variety of concepts in images and video including objects, themes, and more. Trained with over 10,000 concepts and 20M images.
GPT-4 Vision extends GPT-4's capabilities that can understand and answer questions about images, expanding its capabilities beyond just processing text.
Claude 3.5 Sonnet is a high-speed, advanced AI model excelling in reasoning, knowledge, coding, and visual tasks, ideal for complex applications.
Llama-3.2-11B-Vision-Instruct is a multimodal LLM by Meta designed for visual reasoning, image captioning, and VQA tasks, supporting text + image inputs with 11B parameters
Pixtral 12B is a natively multimodal model excelling in multimodal reasoning, instruction following, and text benchmarks with a 12B parameter architecture supporting variable image sizes and long context inputs
The OCR-2.0 model (GOT) is a versatile and efficient optical character recognition system designed to handle diverse tasks, including text, formulas, and charts, through a unified end-to-end architecture.
Multi-model workflow that detects, crops, and recognizes demographic characteristics of faces. Visually classifies age, gender, and multi-culture characteristics.
Multi-model workflow that combines face detection and sentiment classification of 7 concepts: anger, disgust, fear, neutral, happiness, sadness, contempt, and surprise.
A general image workflow that combines detection, classification, and embedding to identify general concepts including objects, themes, moods, etc.
RAG Agent uses GPT-4 Turbo LLM model with ReAct prompting, optimizing dynamic reasoning and action planning.
