Scene Classification with Edge AI
Classifying entire camera frames into scene categories such as indoor vs outdoor, day vs night, crowded vs empty, or room type without detecting individual objects. Used for context-aware IoT devices that adapt behavior based on their environment — adjusting lighting, HVAC, or security modes automatically. Lower compute than object detection since the model outputs a single class label per frame with no localization required.
Hardware Requirements
| Minimum RAM | 128 KB |
| Minimum Flash | 512 KB |
| Sensor Inputs | camera |
| Typical Model Size | 100 KB (quantized int8) |
| Minimum Clock | 80 MHz |
Hardware Guides
No hardware guides for scene classification yet. Use the MCU Checker to find compatible hardware.
Industry Applications
Orchestrate Scene Classification with ForestHub
Your devices run scene classification on-device. ForestHub on your Linux edge gateway ingests their results over MQTT/Modbus/OPC-UA, orchestrates the sense-reason-act loop as an auditable graph, and acts on the line — the LLM is one node among many.