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