You will get a YOLO object detection prototype for your images or videos
Project details
I will build a YOLO-based computer vision prototype for object detection in images or videos.
This project is suitable if you need to detect objects, visualize bounding boxes, test a YOLO model, prepare an inference pipeline, or create a proof-of-concept before scaling the system.
I can help with:
• YOLO inference on images or videos
• Object detection result visualization
• Confidence threshold tuning
• Basic evaluation if labels are provided
• Python scripts or notebooks
• Clean documentation and setup instructions
This service is best for prototypes, demos, research-oriented experiments, and early-stage computer vision validation.
For complex or unclear projects, I recommend starting with the Basic package first. This allows us to validate the data, model, and expected output before moving to a larger implementation.
Package difference:
• Starter is for quick feasibility testing on a few sample images or a short video.
• Standard is for a working YOLO inference pipeline with organized code, outputs, and documentation.
• Advanced includes everything in Standard, plus basic evaluation if labels are provided, result plots/tables, a short report, and a demo output.
This project is suitable if you need to detect objects, visualize bounding boxes, test a YOLO model, prepare an inference pipeline, or create a proof-of-concept before scaling the system.
I can help with:
• YOLO inference on images or videos
• Object detection result visualization
• Confidence threshold tuning
• Basic evaluation if labels are provided
• Python scripts or notebooks
• Clean documentation and setup instructions
This service is best for prototypes, demos, research-oriented experiments, and early-stage computer vision validation.
For complex or unclear projects, I recommend starting with the Basic package first. This allows us to validate the data, model, and expected output before moving to a larger implementation.
Package difference:
• Starter is for quick feasibility testing on a few sample images or a short video.
• Standard is for a working YOLO inference pipeline with organized code, outputs, and documentation.
• Advanced includes everything in Standard, plus basic evaluation if labels are provided, result plots/tables, a short report, and a demo output.
AI Development Type
Deep Learning, Model TuningAI Tools
Keras, OpenCV, PyTorch, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$120
|
Standard
$300
|
Advanced
$650
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 2 |
AI Model Integration | |||
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | |||
Ontology | - | - | - |
Source Code | |||
Taxonomy | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$40About Muhammed
Robotics & Computer Vision Engineer | YOLO, ROS2, SLAM
Dhahran, Saudi Arabia - 2:34 am local time
I am currently a PhD researcher in Robotics and Control at King Fahd University of Petroleum & Minerals (KFUPM), working on robotics, motion planning, state estimation, and active sensing. I also completed an M.Eng. in Electrical and Computer Engineering / Applied AI and Robotics from the University of Ottawa.
I can help with:
• YOLO-based object detection and computer vision pipelines
• Robotics and perception prototypes using Python, MATLAB, ROS2, and Gazebo
• SLAM, GTSAM, and state-estimation examples and simulations
• Research paper implementation in Python or MATLAB
• Technical demos, plots, notebooks, and reproducible code
• Computer vision workflows for inspection, robotics, and data analysis
My previous Upwork projects include YOLOv8-based computer vision, MATLAB/GTSAM/SLAM-related work, and Python scientific scripting. I focus on clear communication, reliable delivery, and well-documented technical solutions.
For complex robotics, computer vision, or research-oriented tasks, I usually start with a small feasibility review or prototype so the client can evaluate progress before committing to a larger scope.
Steps for completing your project
After purchasing the project, send requirements so Muhammed can start the project.
Delivery time starts when Muhammed receives requirements from you.
Muhammed works on your project following the steps below.
Revisions may occur after the delivery date.
Review Requirements and Sample Data
I will review your goal, sample images or video, object classes, expected output format, and any constraints before implementation.
Set Up YOLO Inference Pipeline
I will prepare the Python/YOLO workflow, load the model, configure input/output folders, and run initial detection tests.