Documentation Guide
NE503 docs are organized around roles. Different roles need different content — this page helps you find your starting point: each role lists the core goal, the sections to read (links jump straight to them), and a suggested order. No need to read everything.
Documentation Sections Overview
| Section | Content | For whom |
|---|---|---|
| Product Information | Product positioning, core capabilities, interfaces and specs | Everyone (a 5-minute product overview) |
| Quick Start | Linear loop from unboxing to live (about 10 minutes) | Evaluation / first deployment |
| User Guide | All web console features: image, peripherals, model management, ops, security, wiring | Operations / installation staff |
| Hardware Guide | Board-level hardware: core board, interface board, development and debugging | Hardware engineers |
| Software Guide | System architecture, source build, flashing, version matrix | Platform developers |
| Application Guide | App development (SDK / tutorials / model training), Cookbook recipes, Reference (App / SDK / REST / video / events) | App developers / integrators / model engineers |
| Troubleshooting | Symptom-oriented troubleshooting FAQ | Everyone (look here when something breaks) |
Pick a starting point by role
Evaluator / first deployment
Goal: from unboxing to a live device — verify camera, streams, and AI all work.
The path is the Quick Start (§1–§6, about 10 minutes). Once done, the device is live. For day-to-day use, see the User Guide.
App developer
Goal: build your own AI container app and deploy it to the device.
| Order | Section | What it answers |
|---|---|---|
| 1 | SDK prerequisites | SDK capabilities, how an app gets inference results |
| 2 | First SDK call | Code for subscribing to streams, inference, peripherals, events |
| 3 | Declare permissions | Declaring model and stream permissions in app.yaml |
| 4 | Hello World | Build and run your first app end to end |
| 5 | Cookbook: Parking Lot | What a complete business app looks like |
System integrator
Goal: integrate the device's video streams, AI events, and management APIs into existing systems (NVR / VMS / platforms).
| Order | Section | What it answers |
|---|---|---|
| 1 | Authentication | Logging in for a session token, using the integration key |
| 2 | Video and Imaging | RTSP URLs and parameters for NVR/VMS/FFmpeg |
| 3 | Event integration | Subscribing to AI inference results and device alerts via WebSocket/MQTT |
| 4 | REST API reference | All management endpoints for models, apps, and the device |
Model engineer
Goal: train your own detection model, compile it to HEF, and deploy it to the device NPU.
| Order | Section | What it answers |
|---|---|---|
| 1 | Model sources and choice | Whether preloaded models suffice or you need the full pipeline |
| 2 | Version matrix · factory preload list | What models the device ships with, component version constraints |
| 3 | Model training | YOLOv8 training: dataset, 384×640 input, hyperparameters |
| 4 | Hailo HEF quantized compilation | ONNX → quantization → NMS → HEF end to end |
| 5 | Deploy to NE503 | Import to device, verify loading, run end to end |
Platform developer
Goal: understand and build the entire platform software stack.
| Order | Section | What it answers |
|---|---|---|
| 1 | Four-layer platform architecture | How platform services / HAL / apps are layered |
| 2 | End-to-end data flow | The full path of a frame from sensor to event |
| 3 | Developer guide · Environment setup | Three-repo layout, toolchain, Docker environment |
| 4 | Version build | Building platform components from source |
When something goes wrong
Whatever your role, start troubleshooting in Troubleshooting: a symptom-oriented FAQ (device & network / video & streams / AI & models / apps & containers / events & integration / storage & disks / flashing & peripherals / system & services) organized as symptom → cause → fix.