You will get a custom object detection & tracking system using YOLOv8 and Python
Project details
I build custom object detection and tracking systems using YOLOv8, OpenCV, and Python — delivering fast, accurate, and production-ready solutions. With completed projects including tennis ball tracking and vehicle tracking systems, I know how to handle real-world challenges like occlusion, lighting changes, and multi-object scenarios. Whether you have a dataset or need me to build one from scratch, I deliver clean, documented code that works on video files, live cameras, or CCTV streams.
Machine Learning Tools
BERT, Google AutoML, GPT-3, NLTK, NumPy, Python, Python Scikit-Learn, PyTorch, SciPy, SQL, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$100
|
Standard
$200
|
Advanced
$350
|
|---|---|---|---|
| Delivery Time | 5 days | 7 days | 12 days |
Number of Revisions | 1 | 3 | 5 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 3 | 5 |
Number of Graphs/Charts | 1 | 2 | 5 |
Model Validation/Testing | - | ||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$30 - $50
Additional Revision
+$10About Osama
AI/ML Engineer | Computer Vision | Deep Learning | YOLOv8 | TensorFlow
Karachi, Pakistan - 4:25 pm local time
building computer vision systems, deep learning models, and
end-to-end ML pipelines using Python, TensorFlow, Keras, and YOLOv8.
I'm also a published researcher — my paper on GAN-based medical
image synthesis (Lightweight Harmonic Frequency-Spatial Fusion GAN
for Macular Hole Generation) was accepted at the IEREK International
Conference at the American University of Ras Al Khaimah (AURAK) in
January 2026, indexed in Scopus proceedings.
What I've built:
- Real-time vehicle detection and wheel-counting system using
YOLOv8 + ByteTrack, trained on a custom dataset with live
traffic visualization
- Deep learning classifier for maternal fetal ultrasound images
(brain, heart, abdomen) using CNNs with TensorFlow/Keras
- Art gallery projector system using Kinect v2 + OpenCV, deployed
on Raspberry Pi for real-time depth mapping and canvas detection
I work across the full ML workflow — data preprocessing, model
training, evaluation, and deployment. Currently expanding into
LLMs and Generative AI.
Tools: Python · TensorFlow · Keras · YOLOv8 · OpenCV ·
scikit-learn · pandas · NumPy · ByteTrack · Raspberry Pi ·
SQL · Flask
Steps for completing your project
After purchasing the project, send requirements so Osama can start the project.
Delivery time starts when Osama receives requirements from you.
Osama works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & Strategy
I review your requirements, analyze your sample videos or images, and choose the best model for your case — YOLOv8, YOLOv9, YOLOv10, or RT-DETR — based on speed and accuracy needs.
Data Annotation & Preparation
I annotate your provided images or videos with bounding boxes using Roboflow or LabelImg and prepare a clean, structured dataset ready for training.