Build a Complete Vision Pipeline — Capstone
A production vision system is a chain of models and rules stitched with data contracts. The pieces are already in this phase; the capstone wires them together end-to-end. Design a production vision pipeline that detects objects, classifies them, and emits structured JSON — with every failure path handled. Plug a detector (Mask R-CNN or YOLO), a classifier (ConvNeXt-Tiny), and a data contract (Pydantic) into one service. Benchmark the end-to-end pipeline and identify the first bottleneck (usually preprocessing, then the detector). Ship a minimal FastAPI service that accepts an image upload, runs the pipeline, and returns detections with classifications. Individual vision models are useful; vision products are chains of them. A retail shelf audit is a detector plus a product classifier plus a price-OCR pipeline. Autonomous driving is a 2D detector plus a 3D detector plus a segmenter plus a tracker plus a planner. A medical pre-screen is a segmenter plus a region classifier plus a clinician UI. Wiring those chains is the part that separates a ML prototype from a product. Every interface between models is a new place for bugs. Every coordinate transform, every normalisation, every mask resize is a silent-failure candidate. A pipeline is as strong as its weakest interface. This capstone sets up the minimum viable pipeline: detection + classification + structured output + a serving layer.…
Build a Complete Vision Pipeline — Capstone: A production vision system is a chain of models and rules stitched with data contracts. The pieces are already in…
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