Object Detection
YOLOv10
OpenVINO
PyTorch
GGUF
Safetensors
English
phi3
text-generation
disaster-response
Instructions to use sohail-kustagi/MAAS-Disaster-Response with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- YOLOv10
How to use sohail-kustagi/MAAS-Disaster-Response with YOLOv10:
from ultralytics import YOLOvv10 model = YOLOvv10.from_pretrained("sohail-kustagi/MAAS-Disaster-Response") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
MAAS-LLM: End-to-End Video Benchmark
Test Configuration
- Video: Pella Home Fire Drone Footage (720p)
- Environment: Local PC (x86 CPU Baseline)
- Vision Model: YOLOv10 (PyTorch CPU)
- Reasoning Model: Phi-3 4k Instruct (llama.cpp)
Stage 1: Vanilla Baseline (No LoRA)
- Vision Speed (YOLO): 8.77 FPS
- LLM Reasoning Latency: 26.19s per command
Stage 2: Fine-Tuned LoRA (Disaster Analyst)
- Vision Speed (YOLO): 10.33 FPS
- LLM Reasoning Latency: 29.86s per command
Generated automatically by MAAS-LLM Evaluator for the Arm AI Optimization Challenge.