Phase 2: Object Detection & Segmentation · ~75 minutes · Python
Real-Time Vision — Edge Deployment
Edge inference is the discipline of getting a 90-accuracy model to run at 30 fps on a device with 2 GB of RAM. Every percentage point of accuracy is traded against milliseconds of latency.
Hiring signal: Understanding of real-time vision — edge deployment internals
What you will learn
- Measure inference latency, peak memory, and throughput for any PyTorch model, and read the FLOPs / params / latency trade-off
- Quantise a vision model to INT8 using PyTorch's post-training quantisation and verify accuracy loss < 1%
- Export to ONNX and compile with ONNX Runtime or TensorRT; name the three most common export failures and their fixes
- Explain when to pick MobileNetV3, EfficientNet-Lite, ConvNeXt-Tiny, or MobileViT for an edge constraint
Introduction
Type: Learn + Build Languages: Python Prerequisites: Phase 4 Lesson 04 (Image Classification), Phase 10 Lesson 11 (Quantization) Time: ~75 minutes
Learning Objectives
- Measure inference latency, peak memory, and throughput for any PyTorch model, and read the FLOPs / params / latency trade-off
- Quantise a vision model to INT8 using PyTorch's post-training quantisation and verify accuracy loss < 1%
- Export to ONNX and compile with ONNX Runtime or TensorRT; name the three most common export failures and their fixes
- Explain when to pick MobileNetV3, EfficientNet-Lite, ConvNeXt-Tiny, or MobileViT for an edge constraint
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.
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