The Exponential Growth of Visual Data: Unlocking Intelligent Visual AI Agents with NVIDIA NIM Microservices
Types of Vision AI Models
To build a robust visual AI agent, you have the following core types of vision models at your disposal:
- VLMs
- Embedding models
- Computer vision (CV) models
These models serve as essential building blocks for developing intelligent visual AI agents. While the VLM functions as the core engine of each agent, CV and embedding models can enhance its capabilities, whether by improving accuracy for tasks like object detection or parsing complex documents.
Vision Language Models
VLMs bring a new dimension to language models by adding vision capabilities, making them multimodal. These models can process images, videos, and text, enabling them to interpret visual data and generate text-based outputs. VLMs are versatile and can be fine-tuned for specific use cases or prompted for tasks such as Q&A based on visual inputs.
Embedding Models
Embedding models convert input data (such as images or text) into dense feature-rich vectors known as embeddings. These embeddings encapsulate the essential properties and relationships within the data, enabling tasks like similarity search or classification. Embeddings are typically stored in vector databases where GPU-accelerated search can quickly retrieve relevant data.
Computer Vision Models
CV models focus on specialized tasks like image classification, object detection, and optical character recognition (OCR). These models can augment VLMs by adding detailed metadata, improving the overall intelligence of AI agents.
Few-Shot Classification with NV-DINOv2
NV-DINOv2 generates embeddings from high-resolution images, making it ideal for tasks requiring detailed analysis, such as defect detection with only a few sample images. This workflow demonstrates how to build a scalable few-shot classification pipeline using NV-DINOv2 and a Milvus vector database.
Multimodal Search with NV-CLIP
NV-CLIP offers a unique advantage: the ability to embed both text and images, enabling multimodal search. By converting text and image inputs into embeddings within the same vector space, NV-CLIP facilitates the retrieval of images that match a given text query, enabling highly flexible and accurate search results.
Get Started with Visual AI Agents Today
Ready to dive in and start building your own visual AI agents? Use the code provided in the /NVIDIA/metropolis-nim-workflows GitHub repo as a foundation to develop your own custom workflows and AI solutions powered by NIM microservices. Let the example inspire new applications that solve your specific challenges.
FAQs
Q: What are the different types of vision AI models?
A: The different types of vision AI models include VLMs, embedding models, and computer vision (CV) models.
Q: What is the purpose of VLMs?
A: VLMs bring a new dimension to language models by adding vision capabilities, making them multimodal. They can process images, videos, and text, enabling them to interpret visual data and generate text-based outputs.
Q: How do embedding models work?
A: Embedding models convert input data (such as images or text) into dense feature-rich vectors known as embeddings. These embeddings encapsulate the essential properties and relationships within the data, enabling tasks like similarity search or classification.
Q: What is the purpose of computer vision models?
A: Computer vision models focus on specialized tasks like image classification, object detection, and optical character recognition (OCR). These models can augment VLMs by adding detailed metadata, improving the overall intelligence of AI agents.
Q: How do I get started with visual AI agents?
A: Use the code provided in the /NVIDIA/metropolis-nim-workflows GitHub repo as a foundation to develop your own custom workflows and AI solutions powered by NIM microservices.
