Custom YOLOv8 Object Detection & Computer Vision SystemEyad Arshad
I train custom YOLOv8 and YOLOv11 models on your dataset and deliver a complete detection, segmentation, or tracking pipeline — from raw images to real-time inference.
What you get:
Custom model training on YOUR data — vehicles, products, defects, faces, PPE, wildlife, or any object class you define
Full dataset engineering: annotation review, augmentation strategy, class balancing, and train/val/test splits
Validation report with precision, recall, mAP50, mAP50-95, and confusion matrix analysis
Deployment-ready export: .pt, .onnx, and TensorRT formats optimized for your target hardware
Advanced capabilities:
Multi-object tracking pipelines using DeepSORT or ByteTrack for counting, path analysis, and dwell-time logging
Instance segmentation and pose estimation using YOLOv8-seg and YOLOv8-pose variants
Edge deployment optimization for NVIDIA Jetson, Raspberry Pi, or embedded Linux targets
Industries I have built detection systems for: Traffic management, warehouse safety, retail analytics, manufacturing defect inspection, agricultural monitoring, and sports analysis.
If you have images and a detection problem, send me a sample — I will tell you what is achievable before you commit.
FAQs
Example work
Contact for pricing
Duration1 week
Tags
OpenCV
Python
Image Processing
Machine Learning
computer vision
deep learning
Object Detection
object tracking
yolo
Service provided by
Eyad Arshad Multan, Pakistan
Custom YOLOv8 Object Detection & Computer Vision SystemEyad Arshad
Contact for pricing
Duration1 week
Tags
OpenCV
Python
Image Processing
Machine Learning
computer vision
deep learning
Object Detection
object tracking
yolo
I train custom YOLOv8 and YOLOv11 models on your dataset and deliver a complete detection, segmentation, or tracking pipeline — from raw images to real-time inference.
What you get:
Custom model training on YOUR data — vehicles, products, defects, faces, PPE, wildlife, or any object class you define
Full dataset engineering: annotation review, augmentation strategy, class balancing, and train/val/test splits
Validation report with precision, recall, mAP50, mAP50-95, and confusion matrix analysis
Deployment-ready export: .pt, .onnx, and TensorRT formats optimized for your target hardware
Advanced capabilities:
Multi-object tracking pipelines using DeepSORT or ByteTrack for counting, path analysis, and dwell-time logging
Instance segmentation and pose estimation using YOLOv8-seg and YOLOv8-pose variants
Edge deployment optimization for NVIDIA Jetson, Raspberry Pi, or embedded Linux targets
Industries I have built detection systems for: Traffic management, warehouse safety, retail analytics, manufacturing defect inspection, agricultural monitoring, and sports analysis.
If you have images and a detection problem, send me a sample — I will tell you what is achievable before you commit.
FAQs
Example work
Contact for pricing