---
base_model:
- microsoft/Phi-3-mini-4k-instruct
- jameslahm/yolov10n
language:
- en
license: mit
tags:
- yolov10
- phi3
- object-detection
- text-generation
- disaster-response
- openvino
- pytorch
- gguf
- safetensors
pipeline_tag: object-detection
---

# MAAS Disaster Response Models

This repository contains the heavily fine-tuned computer vision and LLM reasoning models used in the **Multi-Disaster Autonomous Aerial Swarm (MAAS)** project.

## Models Included

### 1. YOLOv10 (Disaster Vision Analyst)
- **Architecture:** YOLOv10 Nano (`best.pt`)
- **Format:** PyTorch and Intel OpenVINO (`best_openvino_model/`)
- **Use Case:** Real-time aerial detection of fires, floods, and stranded persons via WebRTC drone streams.
- **Training:** Fine-tuned over several weeks on a strictly curated disaster dataset to ensure high confidence and minimal false positives in extreme environments.

### 2. Phi-3-Mini (Disaster Reasoning Commander)
- **Architecture:** Microsoft Phi-3-Mini 4k Instruct
- **Format:** GGUF (`phi3-lora.gguf`) & Safetensors LoRA adapters
- **Use Case:** Operates as the autonomous reasoning agent. It intakes telemetry data and the YOLO vision detections to autonomously generate MAVLink navigation coordinates without human intervention.

## Benchmark Results
We evaluated the models on real-world disaster footage (e.g., Pella Home Fire drone footage).

- **Vision Speed (YOLO OpenVINO):** 10.33 FPS on edge hardware
- **LLM Reasoning Latency:** 29.86s per MAVLink command generation

### Performance Benchmark Charts
![YOLO Metrics](benchmarks/yolo_metrics.png)
![Phi-3 Metrics](benchmarks/phi3_metrics.png)

*(For the live interactive web demo, please see the linked Hugging Face Space on my profile.)*