Question 1

How much labelled data do we need to train a computer vision model?

Accepted Answer

It depends heavily on task complexity and how different your use case is from existing pre-trained models. For defect detection or classification tasks that are reasonably close to ImageNet-like images, a few hundred high-quality labelled examples per class can be enough to fine-tune an existing backbone. Novel tasks, unusual camera angles, or highly specific defect types typically need thousands of examples. We audit your existing data first and give you an honest estimate — and if labelled data is scarce, we can design active learning and augmentation pipelines to reduce that requirement.

Question 2

How do you handle rare defects or edge cases that appear infrequently in training data?

Accepted Answer

Class imbalance and rare-event detection are the most common failure modes in production vision systems. We address them through a combination of oversampling, synthetic data generation (using diffusion models or traditional augmentation), loss function weighting, and one-class or anomaly detection approaches for cases where you have few positive examples. We also build the evaluation set to specifically include rare classes so their performance is measured, not averaged away.

Question 3

Can your models run on-device at the edge rather than sending data to the cloud?

Accepted Answer

Yes — and for latency-sensitive, bandwidth-constrained, or privacy-critical applications, edge deployment is usually the right choice. We quantise and prune models to fit the target hardware (NVIDIA Jetson, Raspberry Pi, mobile, or custom embedded), export to ONNX or TensorRT, and validate that accuracy targets hold after optimisation. For hybrid architectures, we can run initial inference at the edge and escalate uncertain predictions to a cloud model.

Question 4

How do you measure model quality beyond top-1 accuracy?

Accepted Answer

Accuracy is almost always the wrong primary metric for production vision tasks. We use metrics matched to the task: mAP and F1 for object detection, IoU for segmentation, precision/recall at your operating threshold for quality inspection where false negatives and false positives have different costs. We also track per-class performance, confidence calibration, and performance across subgroups — lighting conditions, camera angles, time of day — to surface hidden weaknesses before deployment.

Question 5

Do you work with video streams as well as static images?

Accepted Answer

Yes. We build pipelines for both real-time video streams (RTSP, WebRTC, Kafka) and batch video processing, with multi-object tracking, temporal smoothing across frames, and event-based alerting. For edge deployments on live feeds, we optimise the inference pipeline for frame throughput — balancing model size, resolution, and processing speed for your specific camera configuration.

Question 6

How do you keep models accurate as conditions change over time?

Accepted Answer

Production vision models degrade when the real-world distribution drifts from training — lighting changes with seasons, products change appearance, cameras age. We instrument every deployed model with confidence histograms and class-distribution monitoring that alert when drift is detected. We also design retraining pipelines that use production samples flagged as low-confidence for active learning — so the model improves from real-world data rather than requiring a full labelling campaign each cycle.

Computer Vision That Works On Your Data,
In Your Environment

End-to-end computer vision services

Object Detection & Recognition

Image Classification

Image & Instance Segmentation

OCR & Document Intelligence

Video Analytics & Tracking

Defect Detection & Quality Inspection

Pose Estimation & Gesture Recognition

Custom Model Training & Fine-Tuning

Edge & On-Device Deployment

Automation where human inspection doesn't scale

Manufacturing

Retail

Healthcare

Security & Safety

Agriculture

Logistics

How we build and deploy vision models

Problem Definition & Data Audit

Dataset Preparation & Annotation

Architecture Selection & Training

Edge Case Testing & Hardening

Deployment & Drift Monitoring

Have a visual inspection or recognition problem to solve?

Our computer vision stack

What makes our vision systems production-ready

Production accuracy, not benchmark accuracy

Inference optimised for your hardware

Data strategy built in from the start

Evaluation-driven model selection

Edge cases handled before go-live

Compliance Standards That Shape Our Computer Vision Practice

Computer Vision FAQs

Computer Vision That Works On Your Data,In Your Environment