Andrew Ng's VisionAgent: Streamlining Vision AI Solutions

VisionAgent: Revolutionizing Computer Vision Application Development

Computer vision is transforming industries like healthcare, manufacturing, and retail. However, building vision-based solutions is often complex and time-consuming. LandingAI, led by Andrew Ng, introduces VisionAgent, a generative Visual AI application builder designed to simplify the entire process – from creation and iteration to deployment.

VisionAgent's Agentic Object Detection eliminates the need for lengthy data labeling and model training, surpassing traditional object detection methods. Its text prompt-based detection allows for rapid prototyping and deployment, utilizing advanced reasoning for high-quality results and versatile complex object recognition.

Key features include:

• Text prompt-based detection: No data labeling or model training required.
• Advanced reasoning: Ensures accurate, high-quality outputs.
• Versatile recognition: Handles complex objects and scenarios effectively.

VisionAgent surpasses simple code generation; it acts as an AI-powered assistant, guiding developers through planning, tool selection, code generation, and deployment. This AI assistance allows developers to iterate in minutes, not weeks.

Table of Contents

• VisionAgent Ecosystem
• Benchmark Evaluation
• VisionAgent in Action
• 1. Prompt: "Detect vegetables in and around the basket"
• 2. Prompt: "Identify red car in the video"
• Conclusion

VisionAgent Ecosystem

VisionAgent comprises three core components for a streamlined development experience:

1. VisionAgent Web App
2. VisionAgent Library
3. VisionAgent Tools Library

Understanding their interaction is crucial for maximizing VisionAgent's potential.

1. VisionAgent Web App

The VisionAgent Web App is a user-friendly, hosted platform for prototyping, refining, and deploying vision applications without extensive setup. Its intuitive web interface allows users to:

• Easily upload and process data.
• Generate and test computer vision code.
• Visualize and adjust results.
• Deploy solutions as cloud endpoints or Streamlit apps.

This low-code approach is ideal for experimenting with AI-powered vision applications without complex local development environments.

2. VisionAgent Library

The VisionAgent Library forms the framework's core, providing essential functionalities for creating and deploying AI-driven vision applications programmatically. Key features include:

• Agent-based planning: Generates multiple solutions and automatically selects the optimal one.
• Tool selection and execution: Dynamically chooses appropriate tools for various vision tasks.
• Code generation and evaluation: Produces efficient Python-based implementations.
• Built-in vision model support: Utilizes diverse computer vision models for object detection, image classification, and segmentation.
• Local and cloud integration: Enables local execution or utilizes LandingAI's cloud-hosted models for scalability.

A Streamlit-powered chat app provides a more intuitive interaction for users preferring a chat interface.

3. VisionAgent Tools Library

The VisionAgent Tools Library offers a collection of pre-built, Python-based tools for specific computer vision tasks:

• Object Detection: Identifies and locates objects in images or videos.
• Image Classification: Categorizes images based on trained AI models.
• QR Code Reading: Extracts information from QR codes.
• Item Counting: Counts objects for inventory or tracking.

These tools interact with various vision models via a dynamic model registry, allowing seamless model switching. Developers can also register custom tools. Note that deployment services are not included in the tools library.

Benchmark Evaluation

1. Models & Approaches

• Landing AI (Agentic Object Detection): Agentic category.
• Microsoft Florence-2: Open Set Object Detection.
• Google OWLv2: Open Set Object Detection.
• Alibaba Qwen2.5-VL-7B-Instruct: Large Multimodal Model (LMM).

2. Evaluation Metrics

Models were assessed using:

• Recall: Measures the model's ability to identify all relevant objects.
• Precision: Measures the accuracy of detections (fewer false positives).
• F1 Score: A balanced measure of precision and recall.

3. Performance Comparison

Model

Recall

Precision

F1 Score

Landing AI

77.0%

82.6%

Model	Recall	Precision	F1 Score
Landing AI	77.0%	82.6%	79.7% (highest)
Microsoft Florence-2	43.4%	36.6%	39.7%
Google OWLv2	81.0%	29.5%	43.2%
Alibaba Qwen2.5-VL-7B-Instruct	26.0%	54.0%	35.1%

79.7% (highest)

Microsoft Florence-2

43.4%

36.6%

39.7%

Google OWLv2

81.0%

29.5%

43.2%

Alibaba Qwen2.5-VL-7B-Instruct

26.0%

54.0%

35.1%

4. Key Findings

Landing AI's Agentic Object Detection achieved the highest F1 score, indicating the best balance of precision and recall. Other models showed trade-offs between recall and precision.

VisionAgent in Action

VisionAgent uses a structured workflow:

1. Upload the image or video.

2. Provide a text prompt (e.g., "detect people with glasses").

3. VisionAgent analyzes the input.

4. Receive the detection results.

5. Prompt: "Detect vegetables in and around the basket"

Step 1: Interaction

The user initiates the request using natural language. VisionAgent confirms understanding.

Input Image

Interaction Example

"I'll generate code to detect vegetables inside and outside the basket using object detection."

Step 2: Planning

VisionAgent determines the best approach:

• Understand image content using Visual Question Answering (VQA).
• Generate suggestions for the detection method.
• Select appropriate tools (object detection, color-based classification).

Step 3: Execution

The plan is executed using the VisionAgent Library and Tools Library.

Observation and Output

VisionAgent provides structured results:

• Detected vegetables categorized by location (inside/outside basket).
• Bounding box coordinates for each vegetable.
• A deployable AI model.

Output Examples

2. Prompt: "Identify red car in the video"

This example follows a similar process, using video frames, VQA, and suggestions to identify and track the red car. The output would show the tracked car throughout the video. (Output image examples omitted for brevity, but would be similar in style to the vegetable detection output).

Conclusion

VisionAgent streamlines AI-driven vision application development, automating tedious tasks and providing ready-to-use tools. Its speed, flexibility, and scalability benefit AI researchers, developers, and businesses. Future advancements will likely incorporate more powerful models and broader application support.

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