Hire Computer Vision Developer — object detection, OCR, and image pipelines that ship
Vision models fail on the data you never tested: bad lighting, odd angles, blurry phone photos. I build for that — training sets and augmentation that mirror your real conditions, YOLO tuned for real-time detection, OCR tuned to your documents. Models get benchmarked on your worst images, never a clean public dataset.
I'm Omer Muneer Qazi, a Dubai-based Fractional CTO & Solutions Architect with 15+ years of experience and 100+ projects delivered across 6 countries. I’ve shipped defect detection, document OCR, and camera analytics worldwide — pair this with my NLP engineer page for multimodal systems.
Computer vision systems that survive the real world
Object detection & tracking
YOLO-based detection tuned to your classes and camera conditions — trained, validated on your footage, and benchmarked for the latency your line or app actually needs, not a leaderboard score.
OCR & document extraction
Pipelines that read your invoices, IDs, forms, and labels — layout-aware extraction, handwriting-tolerant where it matters, and confidence scores on every field so bad reads get flagged, not trusted.
Image preprocessing pipelines
Consistent resize, normalize, denoise, and augment steps versioned as code — the unglamorous layer that decides whether your model sees the same world in training and in production.
Edge deployment
Models quantized and optimized for Jetson, mobile, or on-prem hardware — running where your cameras are, with the latency and privacy that cloud round-trips can’t give you.
Dataset strategy & labeling
What to collect, how to label it, and how much you actually need — including active learning loops that cut labeling cost by prioritizing the images your model is worst at.
Model monitoring in production
Drift detection on input distributions, accuracy sampling on live predictions, and retraining triggers — because cameras move, lighting changes, and last quarter’s model quietly rots without warning.
From your footage to a deployed model
A structured engagement with no surprises — you’ll always know what’s happening and what’s next.
Feasibility on your footage
Before anything is promised, I test candidate models on your actual images — real lighting, real angles — and report honest accuracy with failure modes attached.
Dataset & model build
Labeling specs, augmentation strategy, and model training with your data — iterated against a held-out test set that mirrors deployment, never the training distribution.
Pipeline & edge integration
Preprocessing, inference, and post-processing wired into your stack or edge device — with latency budgets met and measured, not hoped for.
Monitoring & retraining loop
Drift alerts and scheduled re-evaluation keep the model honest as conditions change — with a clear retraining playbook your team can run.
Why hire through a fractional CTO
Vision projects die between a demo on stock photos and your actual cameras. I close that gap: real data first, honest benchmarks, models sized for your hardware. Fifteen years shipping production systems means the pipeline around the model gets built, not bolted on later.
Dubai-based, working worldwide across 6 countries and 100+ projects. One senior engineer owns data, model, and deployment — no handoffs between teams that never meet. Talk to me about your cameras.
Computer vision developer FAQs
Which model should we use — YOLO, or something newer?
Whichever clears your accuracy and latency bar on your footage. I benchmark YOLO variants and alternatives against your data and hardware — the right answer is measured, and it changes as models improve.
How much labeled data do we need?
Less than most vendors claim. With pretrained backbones and active learning, strong results often come from hundreds of well-chosen images, not tens of thousands. I audit what you have before recommending any labeling spend.
Can models run on-device instead of the cloud?
Usually yes. Quantized models run on Jetson, modern phones, and industrial PCs — cutting latency and keeping footage private. I profile your hardware first and only recommend cloud inference where on-device genuinely falls short.
Our documents are in Arabic — does OCR handle that?
With the right engine and tuning, yes. Arabic script needs models trained on right-to-left text and connected letterforms — I evaluate OCR engines on your actual documents, including mixed Arabic-English pages, before committing.
How do you prevent model drift after deployment?
Input distribution monitoring, periodic accuracy sampling against fresh labels, and retraining triggers with thresholds you approve. Drift is treated as an operational metric with an owner and a playbook, not a surprise.
Put cameras to work, not on a shelf
Send sample footage or documents — I’ll tell you what’s feasible, what it costs, and what to watch out for.