Embedded AI Devlog

Milestone 3: Training a Lightweight Clarity Model + Exporting to RKNN (RV1126)

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Milestone: 3 — Train → Export ONNX → Convert RKNN → Ready for On-board Validation 1) What Milestone 3 Is About Milestone 3’s goal is straightforward: At the end of this milestone, I should have a clean, reproducible conversion pipeline and a ready-to-test model on board. 2) Current Status (What’s Done) Training completed (baseline clarity_v0) […]

Milestone 3: Training a Lightweight Clarity Model + Exporting to RKNN (RV1126) Read More »

Milestone 2 Done: MPIIGaze Subset + Clarity Labeling Pipeline (5-Level Ordinal → Normalized Score)

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What I completed in Milestone 2 This milestone focuses on building a reproducible dataset + labeling pipeline for my capstone project: eye image clarity assessment on RV1126 (RKNN runtime). The goal is not “training the best model yet”, but making sure the entire data pipeline is solid, auditable, and ready for deployment-oriented iteration. Dataset choice:

Milestone 2 Done: MPIIGaze Subset + Clarity Labeling Pipeline (5-Level Ordinal → Normalized Score) Read More »

Capstone Kickoff: Connecting Windows, Ubuntu, and RV1126 + Verifying the RKNN Runtime

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TL;DR I’m building an eye-image clarity assessment system and deploying it on Rockchip RV1126 (NPU-enabled). This kickoff milestone focuses on engineering fundamentals: multi-host connectivity, board access, and RKNN runtime verification on the target device. Result: the RV1126 can run RKNN inference successfully with stable performance (~28–31 ms avg, ~32–36 FPS in the provided demo). 1.

Capstone Kickoff: Connecting Windows, Ubuntu, and RV1126 + Verifying the RKNN Runtime Read More »