YOLO-OptiMob: A Pipeline for Optimizing YOLO11 Models for Edge Deployment

Abstract

"This paper introduces YOLO-OptiMob , a comprehensive pipeline for optimizing YOLO11 models for deployment on edge devices. The process begins with creating and preprocessing a custom dataset featuring seven object classes: bike, car, cat, dog, person, handbag, and water bottle. The YOLO11 model is trained on this dataset and optimized using L1 unstructured pruning, with rates of 30%, 40%, and 50% evaluated. Based on the results, a 40% pruning rate was selected as it offered the best balance between model size reduction and accuracy retention. Post-training INT8 quantization further compresses the model, reducing its size from 11.4 MB to 2.5 MB. The optimized model is then converted into TensorFlow Lite (TFLite) format, ensuring compatibility with Android-based edge devices. This work presents a practical pipeline for efficiently adapting YOLO11 to resource-constrained environments, achieving significant size reduction while maintaining high detection accuracy."