You will get Real-Time Object Detection System using YOLOv8 & OpenCV (Python)
Project details
My object detection solutions are built with precision, speed, and real-world usability in mind. Leveraging state-of-the-art models like YOLOv8 and frameworks like OpenCV, I deliver highly optimized, real-time detection systems tailored to your specific dataset and application ā from security and automation to sports analytics or UAV-based systems.
What makes my service unique:
š Custom training on your own dataset for maximum accuracy.
š§ Deep understanding of model tuning and augmentation for performance across real-world environments.
š» Fully documented code with clean structure, reusable functions, and step-by-step explanations.
š Optional visualizations, performance metrics, and deployment-ready formats (e.g., ONNX, TensorRT).
š„ Optional video demo or screen recording to help you or your team understand how to use the system.
Whether you're building an AI-driven surveillance tool or integrating object detection into a mobile or robotics platform, I ensure high accuracy, speed, and clarity in every project.
What makes my service unique:
š Custom training on your own dataset for maximum accuracy.
š§ Deep understanding of model tuning and augmentation for performance across real-world environments.
š» Fully documented code with clean structure, reusable functions, and step-by-step explanations.
š Optional visualizations, performance metrics, and deployment-ready formats (e.g., ONNX, TensorRT).
š„ Optional video demo or screen recording to help you or your team understand how to use the system.
Whether you're building an AI-driven surveillance tool or integrating object detection into a mobile or robotics platform, I ensure high accuracy, speed, and clarity in every project.
Machine Learning Tools
ChatGPT, GitHub Copilot, GPT-3, Keras, MATLAB, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Scrapy, TensorFlowWhat's included
| Service Tiers |
Starter
$40
|
Standard
$80
|
Advanced
$150
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 2 | 4 | 0 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | |||
Source Code |
About Syed Shumail
Telecom & AI Engineer | Deep Learning, ML, RL, Computer Vision, Python
Rawalpindi, PakistanĀ - 6:25 pm local time
š” Core Expertise:
Reinforcement Learning (RL), Deep Learning & Machine Learning
Computer Vision (YOLO, OpenCV, Object Detection & Tracking)
Python & MATLAB Programming
Signal Processing & Predictive Analytics
Antenna Design & Simulation using HFSS
Wireless Communication System Modeling
CCNA-Level Networking (Routing, Switching, VLANs, Subnetting, Troubleshooting)
I've worked on hands-on projects ranging from AI-based decision systems to telecom signal simulations, antenna modeling, and network configuration using Cisco Packet Tracer. My interdisciplinary approach helps me craft practical solutions that combine AI intelligence with engineering precision.
Whether it's building RL-based agents, designing custom antennas, training deep learning models, or configuring robust networks, I bring innovation, clarity, and commitment to every project.
Letās collaborate to bring your AI, telecom, or networking ideas to life ā with the power of modern intelligence and engineering expertise.
#TelecomEngineering #MachineLearning #ReinforcementLearning #ComputerVision #PythonDeveloper #CCNA #HFSS #SignalProcessing #DeepLearning #AIinTelecom #AntennaDesign #NetworkEngineer
Steps for completing your project
After purchasing the project, send requirements so Syed Shumail can start the project.
Delivery time starts when Syed Shumail receives requirements from you.
Syed Shumail works on your project following the steps below.
Revisions may occur after the delivery date.
Requirement Gathering
Understand client use case: e.g., security camera, product detection, UAV surveillance, sports tracking, etc. Clarify platform: PC, embedded (e.g., Jetson Nano), web, mobile, etc. Determine number and type of objects to detect. Ask for dataset.