camovers/Moving-price-ai

Model Description The Mover's Estimator AI is a cutting-edge multimodal Large Language Model (LLM) designed to revolutionize the moving and logistics industry. This model assists moving estimators in accurately counting and identifying items within images of residential or commercial spaces, streamlining the quote generation process and enhancing estimation precision.

At its core, this system integrates powerful image recognition capabilities with the advanced reasoning of a large language model. It takes visual input (an image) and translates it into a detailed, itemized list of movable objects, which can then be used to inform moving quotes.

Architecture Overview The system leverages a multi-component AI architecture:

Vision Component: For robust image understanding and object detection, the model utilizes a sophisticated vision-enabled LLM (e.g., gemini-2.5-flash-preview-05-20 or similar powerful vision models). This component is responsible for analyzing raw pixel data and identifying various items.

Language & Reasoning Component: Llama 3.2 serves as the foundational Large Language Model (LLM) for the textual processing, reasoning, and structured output generation. After the vision component identifies items, Llama 3.2 can further categorize, count, and present this information in a human-readable and actionable format suitable for quote estimation. It provides the intelligence to interpret the visual findings into practical moving insights.

Intended Uses Moving Companies & Estimators: To quickly assess the contents of a room or property for moving quotes.

Logistics & Inventory Management: For rapid item counting and cataloging in various settings.

Insurance Adjusters: To document and estimate belongings for claims.

Property Management: For pre-move inspections and inventory checks.

Primary Use Cases Automated Inventory Creation: Automatically generate a list of furniture, boxes, appliances, and other items from uploaded photos.

Quote Estimation Support: Provide a quantitative basis for calculating moving costs, reducing manual effort and potential human error.

Efficient Pre-Move Assessments: Conduct virtual assessments with greater speed and accuracy.

How to Use Users can upload an image (e.g., a photo of a living room, bedroom, or storage unit) via a user interface. The model processes the image to identify significant movable items. The output is a descriptive text detailing the recognized objects, often categorized and counted.

Conceptual Python example (simplified for demonstration)

from transformers import pipeline

This is a conceptual pipeline. The actual implementation involves calling external APIs

or running a complex multimodal model locally.

In a real scenario, you'd use a multimodal model that accepts image and text input.

For example, if fine-tuned on Llama 3.2 with a vision adapter.

Hypothetical pipeline for Movers Estimator AI

estimator_pipeline = pipeline( "visual-question-answering", model="your-mover-estimator-model", # Placeholder for the actual Hugging Face model ID revision="main" )

image_path = "path/to/your/room_image.jpg" prompt = "Identify all significant movable items in this image that a moving company would need to transport. List them clearly and categorize them if possible (e.g., furniture, boxes, appliances, decor). Try to count multiple instances of the same item."

The actual inference would involve converting the image to the model's expected format

and packaging it with the prompt.

result = estimator_pipeline(image=image_path, question=prompt)

print(result)

Expected Output (example):

"Furniture: 1 sofa, 2 armchairs, 1 coffee table. Boxes: 5 medium boxes, 2 large boxes. Appliances: 1 television. Decor: 3 picture frames, 1 vase."

Limitations Image Quality: Performance is highly dependent on image clarity, lighting, and resolution. Blurry or poorly lit images may result in incomplete or inaccurate recognition.

Occlusion: Items partially hidden behind other objects may not be fully identified or counted.

Specificity: While capable of broad categorization, distinguishing between very similar items (e.g., different types of chairs) without fine-grained training data may be challenging.

Contextual Understanding: The model identifies items but does not inherently understand the sentimental value or fragility of an item, which still requires human assessment.

Potential Improvements & Roadmap Enhanced Item Categorization: Further fine-tuning on diverse moving-specific datasets to improve categorization and item specificity.

Volume Estimation: Integrating volumetric data or estimations based on recognized items to provide more accurate space requirements.

Damage Assessment: Developing capabilities to identify potential damage or special handling requirements for certain items.

User Feedback Loop: Implementing a mechanism for movers to correct or refine the AI's output, which can then be used to retrain and improve the model.

Credits

Developed by and for https://bestcaliforniamovers.com/