Need an accurate, high-FPS computer vision pipeline to detect, track, and count custom objects across images, recorded videos, or live CCTV/RTSP streams?

Instead of just handing over raw model weights, I deliver an end-to-end YOLOv8 object detection and tracking system built with Python, PyTorch, and OpenCV—ready to run locally or in production.

COMMON USE CASES: • CCTV Surveillance, Intrusion & PPE Safety Compliance • Industrial Automation, Analogue Gauge Transcription & Defect Inspection • Agriculture Leaf/Crop Disease Detection & Grading • Vehicle Traffic Counting & Retail Analytics

MY 4-STEP PROCESS:

Dataset Audit & Prep: Inspect annotations, apply Roboflow/albumentations preprocessing, and split train/val sets.

Custom YOLOv8 Training: Train and hyperparameter-tune YOLOv8 on GPU for optimal precision and recall.

Tracking & Inference Pipeline: Integrate OpenCV and ByteTrack/BoT-SORT for unique object IDs, counting, and zone alerts.

Deployment & Handoff: Package into a Python CLI, FastAPI REST service, or Streamlit web app with full setup docs.

WHAT I NEED FROM YOU TO KICK OFF: • Link to your labeled dataset (or raw images/videos if you need custom annotation added to scope). • List of target classes to detect/count. • Target hardware environment (Cloud GPU, CPU, or Edge device) and desired output format.