Using large models to sanity-check edge detections Introduction Audio datasets are fragile. Unlike images or text, you cannot visually skim through thousands of audio samples and immediately know whether they belong to the right class. A barking dog, a metal clank, or background human speech can sound deceptively similar in
Problem Modern pet‑health systems increasingly rely on thermal cameras to extract physiological signals such as eye temperature. The challenge is that thermal sensors are low‑resolution and almost never share the same optical center, field of view, or distortion model as the RGB camera doing the visual perception. In
Introduction Transfer learning usually feels like a shortcut that just works. Pretrain on a large dataset, fine-tune on your task, and let scale do the heavy lifting. That approach worked well for us in vision - but audio broke that assumption in a very specific, painful way. At Hoomanely, we
Introduction Audio is one of the hardest signals to work with in real-world ML systems. Unlike images, it is continuous, noisy, and deeply tied to the environment it is captured in. At Hoomanely, we build smart pet products that live inside homes - kitchens, living rooms, and feeding areas -
Introduction Building machine learning systems for the edge sounds exciting—until you realize that most real-world edge problems begin with a simple, uncomfortable truth: you don’t have labeled data. Not enough of it. Not the right kind. And certainly not data that reflects the messy environments your devices will
Intro One of the hardest problems in smart pet devices isn’t sensing - it’s understanding context. At Hoomanely, our EverBowl feeding system can reliably detect when a dog is eating using weight sensors, proximity sensing, and on‑device vision. But knowing what the dog is eating is a
Introduction Landmark detection models often look impressive during offline evaluation - clean datasets, sharp images, balanced lighting, and tidy annotations. But the real world is rarely that polite. At Hoomanely, we deploy dog‑face landmark detection models (eyes, nose, facial keypoints) on always‑on indoor devices mounted on feeding bowls.
Introduction Over the past decade, cloud inference has powered most large-scale machine learning systems — from recommendation engines to speech assistants. But as devices at the edge get faster, cheaper, and more privacy-aware, a hybrid pattern is emerging: compute just enough on the device to protect the user and reduce noise,
Introduction On edge devices, every millisecond and every megabyte matters. Audio models must run reliably on low-power CPUs, handle noisy real-world environments, and deliver consistent predictions without draining battery or storage. Traditional deep audio models—large transformers or multi-million-parameter networks—struggle here. They are powerful, but far too heavy for
Introduction Running Python applications on edge devices is fundamentally different from deploying them on servers or developer laptops. These devices - whether embedded compute modules, IoT gateways, or sensor hubs - operate under strict storage ceilings and must deploy updates reliably without exhausting disk space. At Hoomanely, our edge systems