model · open source
Computer Vision with YOLO
Computer Vision built on YOLO, chosen where it genuinely fits, and swapped where it does not.
- Category
- model
- Vendor
- Open source
- Alternatives we also use
- 7
Why YOLO for this
Vision models fail on lighting, not on architecture. We collect from your actual line, in your actual conditions, because a model trained on clean images will not survive a real shift.
YOLO is strongest at fast enough for real-time video on modest edge hardware. For computer vision that matters because the failure modes of this kind of system tend to cluster exactly there.
The honest trade-off: small or highly overlapping objects need a different architecture. We say that up front because a stack chosen for fashion rather than fit becomes someone's migration project two years later. We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.
You own the code, the models where they are open-weight, and the documentation to run it without us.
The honest assessment
- What it is
- Real-time object detection architecture, the workhorse of applied computer vision.
- Strongest at
- fast enough for real-time video on modest edge hardware
- Trade-off
- small or highly overlapping objects need a different architecture
- Category
- model
We are not a reseller for YOLO and hold no commission on this choice. Where a different option fits your workload better, the recommendation will say so. That is the entire value of asking us.
What is included
- Data collection protocol and labelling workflow
- Model training against your real conditions and lighting
- Edge deployment with offline tolerance
- Precision and recall reported per defect class
- Integration with MES, PLC or alerting systems
- Retraining pipeline as conditions drift
Questions
How much training data do we need?
It depends on defect variability, but a few hundred well-labelled examples per class is a realistic starting point. We design the collection protocol first so the data you gather is actually usable.
Does it run without internet?
Yes. We deploy at the edge with offline tolerance, syncing results when connectivity returns, essential in most plant environments.
What accuracy can we expect?
We report precision and recall per defect class against a held-out set from your line, rather than a single headline number. The honest figure varies by class and we show which ones are hard.
Alternatives for computer vision
Same capability, different stack. Each page states its own trade-off.
What else we build on YOLO
Building with YOLO?
Bring us the workload and we will tell you whether this is the right stack for it.
Or email bd@dtrasglobal.com · call +91 74118 77878