GPU Grant Request for Wildfire Scene Analysis System

Project Overview

I've developed an AI-powered wildfire analysis system using the Qwen2-VL vision-language model (4.42GB). The system analyzes wildfire images and provides detailed descriptions in English about fire intensity, affected areas, and environmental conditions.

Space URL: https://huggingface.co/spaces/hiko1999/wildfire_teller
Model Used: hiko1999/Qwen2-Wildfire-2B (fine-tuned Qwen2-VL)

Why This Project Matters

Wildfires are becoming increasingly frequent and severe due to climate change. This tool can help:

• Emergency responders quickly assess fire situations from aerial/ground images
• Environmental agencies monitor wildfire spread and impact
• Researchers study wildfire patterns and behaviors
• News organizations provide accurate fire situation reports to the public

Current Limitation

The model currently runs on CPU basic hardware, which results in:

• 30-40 seconds inference time per image
• Poor user experience for real-time analysis
• Impractical for emergency use cases where speed is critical

Why I Need GPU

GPU acceleration (ZeroGPU or similar) would reduce inference time to 2-3 seconds, making this tool practical for real-world emergency scenarios. The 4.42GB model size requires GPU memory for efficient inference.

Project Details

• Open Source: The Space is public and freely accessible
• No Commercial Use: This is a side project for public good
• Active Development: I'm continuing to improve the model and interface
• Community Contribution: All code and model weights are publicly available

Expected Usage

• Educational demonstrations
• Research purposes
• Public awareness about wildfire AI analysis
• Potential emergency response tool testing

Commitment

I commit to:

• Keep the Space public and free
• Properly credit Hugging Face in the Space description
• Share learnings with the community
• Not use the granted resources for commercial purposes

Thank you for considering my application. This GPU grant would make a significant difference in the project's real-world applicability.

Best regards,
hiko1999