Cly

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

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) […]

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

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:

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

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.

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Real-Time Face Gate with OpenCV + OpenVINO ( USB Camera, Rpi4)

This post documents a reproducible mini-project: a real-time “face gate” prototype built with OpenCV for camera capture and OpenVINO for inference. Source code:<CashStolen/rpi-myriad-face-reg> 1)pipeline overview The core pipeline is: This design keeps the code modular and makes it easy to swap models or tune thresholds. 2)environment Install dependencies:(the requirement.txt lies in my git project) 3)models

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How to Batch Convert JPG/PNG Images to WebP Using Python (Command-Line Tool Included)

1. Introduction — Why Convert to WebP? I recently optimized my website’s mobile performance and found that images were the main bottleneck. The PNGs and JPGs are putting great pressure on the mobile processing of mobile users which may cause laggy in your website browsing. But as there are so many images to process, it’s

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