Raspberry Pi Deploys Custom Keypoint Detection Model
Sony Semiconductor Solutions publishes Part 2 of its tutorial series showing how to create and deploy custom keypoint detection models on the Raspberry Pi AI Camera. The guide walks through training with posenet_arrow.ini on an arrow dataset (default 50 epochs), quantizing with Edge-MDT to produce posenet_arrow_quantized.tflite and packerOut.zip, and converting to a network.rpk package for IMX500 deployment. Developers can retrain for other objects and deploy efficient, quantized models on-device for pose estimation and custom keypoint tasks.
Key Points
- 1Provides end-to-end tutorial to train and deploy custom keypoint models on Raspberry Pi AI Camera.
- 2Uses quantization and Edge-MDT to produce memory-efficient models suitable for IMX500 AI Camera deployment.
- 3Enables practitioners to detect custom object keypoints, deployable as .tflite and network.rpk packages on-device.
Scoring Rationale
Practical, end-to-end edge deployment guide with official tooling; limited novelty because it's an implementation tutorial rather than research.
Sources
Public references used for this report.
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