When to go deep in Computer Vision... and how
summary
Decide by specifics: a quick OpenCV plugin for Unity baseline often runs on phone/hololens and uses cascade detectors for face/hand detection; measure against a stated metric (e.g. aim for 0.95 accuracy or F1, or a reported 0.97 baseline) and required latency (real time ~25–30 fps). Check data constraints (RGB benefits from ImageNet pretrained models ~600 samples/class), deployment limits (CPU only, memory), and supported runtimes like tensorflow lite, CoreML or ONNX before building heavier CNNs.
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chapters (9)
- 0:00Computer vision & deep learning
- 5:32Traditional vs deep learning
- 12:40Constraints in CV projects
- 19:08Baseline design & performance
- 25:58Instagram fashion retrieval
- 27:43Mobile real-time effects
- 32:46From traditional to deep learning
- 35:16Recap & implementation steps
- 36:55Q&A: Baselines & labeled data
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