YOLO.
YOLO is a family of real-time object detection models that locate and classify multiple objects in a single network pass. The single-stage design is what makes detection fast enough for video.
Where YOLO fits on a long engagement.
Single-pass detection is what makes real-time video practical. Two-stage detectors are more accurate on difficult cases but too slow for frame-rate processing, and for most applications — counting, tracking, safety monitoring — the speed matters more than the last few points of accuracy.
Custom detection needs annotated data, and the annotation is the project. Thousands of images with accurate bounding boxes is significant effort, and annotation quality directly determines model quality — inconsistent boxes produce a model that is inconsistent in exactly the same way.
What an assigned team does with YOLO.
Deployment target shapes the whole design. A model running on a server can be large; one running on an edge device has hard memory and compute limits, and quantisation or a smaller variant is required rather than optional.
Deciding that before training rather than after is basic sequencing, and it is part of the scoping described in how an assignment runs.
What we use YOLO for.
- Detection at video frame rate Single-pass inference fast enough for live streams.
- Annotation quality treated as the deliverable Consistent bounding boxes, because the model inherits their inconsistency.
- Model sized for the deployment target Edge constraints decided before training rather than discovered at deployment.
How YOLO capacity is assigned.
Object detection work is assigned inside AI capacity, with annotation effort scoped honestly as the largest part of the project.
Tell us what your roadmap needs YOLO for.
A service delivery manager replies with the disciplines we would assign, the monthly capacity and what the first month looks like.
Loading the contact form… You can also email hello@azendo.co.
We reply within one working day. No obligation, and no newsletter.