Optimization Strategies for Object Detection Models

This repository is dedicated to adding different optimization strategies for object detection models. The aim is to enhance real-time inference capabilities while balancing speed and accuracy, even with limited hardware resources.

Contents

1. YOLOv8 for Real-Time Inference
   Optimizing YOLOv8 to achieve real-time inference with reduced hardware requirements. This includes techniques to balance speed and accuracy using fewer hardware resources.
   Go to YOLOv8 Optimization Documentation

   Go to YOLOv8 Optimization Code

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2. Surveillance Setup with 4 Video Sources
   A detailed explanation of setting up a surveillance system with four concurrent video feeds. This setup demonstrates how multiple video sources can work concurrently, optimized for real-time processing.
   Go to Surveillance Setup Documentation

   Go to Surveillance Setup Code

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3. 𝐘𝐎𝐋𝐎-𝐁𝐚𝐭𝐭𝐥𝐞𝐟𝐢𝐞𝐥𝐝 ⚠️🔥
   Compare YOLO models and find out the best one for your application. Try this on Huggingface Spaces

   𝐘𝐎𝐋𝐎-𝐁𝐚𝐭𝐭𝐥𝐞𝐟𝐢𝐞𝐥𝐝 Code