44 citations · 88 across the 5 of their papers we have counts for
6 papers
Device-First Feedback: Toward Mobile-Native LLM-Driven Neural Architecture Search
Saif U Din, Muhammad Ahsan Hussain, Radu Timofte +1
Deploying convolutional neural networks generated by large language models (LLMs) on real mobile hardware requires more than GPU validation accuracy: INT8 TensorFlow Lite export, d…
LEMUR 2: Unlocking Neural Network Diversity for AI
Tolgay Atinc Uzun, Waleed Khalid, Saif U Din +17
Existing NAS benchmarks (e.g., NAS-Bench, NATS-Bench) cover only narrow, task-specific regions of the architectural design space and lack cross-domain or deployment-aware evaluatio…
A Review of YOLOv12: Attention-Based Enhancements vs. Previous Versions
Rahima Khanam, Muhammad Hussain
The YOLO (You Only Look Once) series has been a leading framework in real-time object detection, consistently improving the balance between speed and accuracy. However, integrating…
YOLOv11: An Overview of the Key Architectural Enhancements
Rahima Khanam, Muhammad Hussain
This study presents an architectural analysis of YOLOv11, the latest iteration in the YOLO (You Only Look Once) series of object detection models. We examine the models architectur…
What is YOLOv5: A deep look into the internal features of the popular object detector
Rahima Khanam, Muhammad Hussain
This study presents a comprehensive analysis of the YOLOv5 object detection model, examining its architecture, training methodologies, and performance. Key components, including th…
YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Vision
Muhammad Hussain
This paper presents a comprehensive review of the evolution of the YOLO (You Only Look Once) object detection algorithm, focusing on YOLOv5, YOLOv8, and YOLOv10. We analyze the arc…