CVLab Pro — Interactive Predict & Watch Computer Vision Suite

2. Smart City Traffic Camera

YOLOv11 Detection + ByteTrack Multi-Object Tracking + Virtual Tripwire Speed & Counting

Arch: YOLOv11m + ByteTrack Annotation: 2D AABB + Polygon

Drag vehicles or click a hypothesis below to test what happens

STAGE 1 CHALLENGE — GUESS WHAT HAPPENS

Loading engineering dilemma...

Confidence Threshold: 0.45

0.100.500.95

NMS IoU Threshold: 0.50

MinMidMax

Live Dynamic Results & Automated Actions

SYSTEM ARMED

Target & Defect Inspection

CLICK OBJECT TO INSPECT

Click any vehicle, license plate, defect, or person on the canvas to inspect its real image crop, neural network feature patch, and diagnostic telemetry.

Neural Network Engine & Layer Diagnostics

YOLOv11m

Live Activation:

Tensor:[1, 256, 80, 80]

Receptive Field:11×11 px (S4)

Kernel / Op:Conv2D (3×3) + SiLU

Architecture: CSPDarknet + PAN-FPN + Decoupled Head

Active Layer: Backbone (Conv 3×3, Stride 2)

Mathematical Role: Extracts multi-scale gradient contours and spatial features.

⚡ Normal tensor feed: No gradient starvation detected.

3D Neural Pipeline Flow

Input [1,3,640,640] → Head

Orbit: Left Click Drag • Zoom: Scroll • Pan: Right Click