The Model Is the Easy Part: What a Real-Time Computer Vision Product Actually Takes
I used to think computer vision was a model problem. It is a pipeline problem, and the model is the easy part. Here is the pattern I keep seeing. A te…
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Testing Face Embedding Nearest Neighbor Search with OpenCV SFace
Testing Face Embedding Nearest Neighbor Search with OpenCV SFace Hello, everyone. Face recognition can sound like a big topic, but technically it can …
Vision Language Models — When AI Learns to See and Talk (Part 3 of 3)
Originally published on my blog . Cross-posted here with a canonical link. This is Part 3 of a 3-part series on the transformer revolution in vision a…
Don't Buy It, Build It: Crafting an Open-Source, Edge-Computing AI Dashcam
Preface Every day, thousands of creative ideas sit completely dormant on workshop shelves around the world. A high-performance microprocessor sits in …
The SDXL VAE overflow that decoded black images in fp16
TL;DR: The SDXL VAE decoder pushes activations past 65504, the max value fp16 can hold, so the last decode step overflows to inf and you get a fully b…
Winograd convolutions cost us 2 mAP and we didn't notice for a month
TL;DR: We turned on Winograd convolution to shave latency off a pedestrian detector running on a Cortex-A53, got a clean 18% speedup, and silently los…
Image Reconstruction Using Deep Learning: A Complete Guide
A comprehensive guide covering history, techniques, datasets, algorithms, tools, real-world applications, and final year project ideas for image recon…
An AI Module Smaller Than a Smartphone Photo. Are We Underestimating the Computer We Carry Every Day?
The Idea and the Main Engineering Challenges Recently, I released a new offline AI feature for my Android application as a separate module. The entire…
Our event-camera detector lost 6 mAP to a badly chosen accumulation window
TL;DR: We spent three weeks chasing a 6 mAP regression in an event-camera object detector. The model was fine. The bug was the accumulation window we …
QAT vs PTQ on our edge vision model: 6 months of A/B data
TL;DR: We ran post-training quantisation (PTQ) and quantisation-aware training (QAT) side by side on the same defect-classification model deployed on …
How I Built a Drone-Based Crack Detection Pipeline on AWS
I needed to build a pipeline that takes drone footage of infrastructure (bridges, facades, roads), detects surface defects like cracks and corrosion, …
I Replaced My $500 GPU with a $75 Raspberry Pi: How Gemma 4 Makes Computer Vision 10x Cheaper
This is a submission for the Gemma 4 Challenge: Write About Gemma 4 I Replaced My $500 GPU with a $75 Raspberry Pi: How Gemma 4 Makes Computer Vision …