Case Study
Computer Vision Training Pipeline (YOLO/SAM)
Dataset to training to evaluation with reproducibility and deployment readiness.
Overview
Problem
CV work fails when data, training, and evaluation are not repeatable or measurable.
Solution
A training pipeline that produces reproducible runs, publishes metrics, and packages models with environment/version metadata.
Results
- Repeatable training jobs
- Clear evaluation artifacts (mAP, PR curves)
- Deployment-ready packaging
Implementation Notes
- Pinned dependencies and explicit seeds for reproducibility.
- Dataset versioning and annotation audit trails.
- Evaluation is a first-class gate, not a spreadsheet afterthought.
Architecture
Dataset -> Train -> Evaluate -> Package
Diagram placeholder (16:9)
Key components
- Dataset pipeline
- Training loop
- Evaluation suite
- Artifacts store
- Deployment packaging
Stack
YOLOSAMPythonData pipelinesExperiment tracking