Problem
Computer vision models are often hard to inspect unless you are comfortable with scripts and raw model output. YOLOPv2-Studio turns that workflow into a more approachable interface for object detection, lane detection, and drivable-area segmentation demos.
Approach
The project uses Python with Flask, OpenCV, PyTorch, and supporting scientific packages. The studio provides upload flows, demo media, webcam processing, configurable thresholds, task toggles, and an FPS display for practical model interaction.
Outcome
The Flask interface makes YOLOPv2 output easier to explore through media upload, webcam processing, and configurable detection controls. Setup documentation and a visual workflow reduce the effort required to run and inspect the model locally.