Computer Vision
Relytic designs, trains, evaluates, optimizes, and deploys computer-vision systems for image and video workflows where measurable performance matters.
The problem
Computer vision is useful when a business process depends on people repeatedly looking at images or video to identify, classify, measure, inspect, or track something.
Common challenges include:
The model architecture is only one part of the solution.
Locate and identify objects in images, video, aerial imagery, or other visual data.
Classify images or regions into the categories required by the workflow.
Identify objects or regions at pixel level when bounding boxes are not precise enough.
Analyze sequences over time, detect events, track movement, estimate pose, or extract structured information from video.
Build systems that identify relevant conditions, anomalies, defects, or objects for industrial and operational workflows.
Process large-scale visual data where objects may be small, densely distributed, or affected by viewpoint and environmental variation.
Export, optimize, serve, and monitor models in the environment where they actually need to run.
System anatomy
When a vision system fails, switching architecture is not always the right answer. We use structured error analysis to understand the failure modes first.
The findings can drive targeted changes to the dataset, augmentation strategy, labeling, model architecture, training procedure, or deployment configuration.
Whole-system quality
A more accurate model is not useful if it cannot meet the operational requirements of the application.
Evidence
Three projects show how the engineering approach changes across industrial imagery, consumer video, and medical AI.
01
84.5% → 92.0%mAP@50
Model benchmarking, structured error analysis, and targeted data improvements supported a move from YOLOv8 to RF-DETR Large.
Production work also covered Airflow, MLflow, ONNX, TensorRT experiments, pruning, and NVIDIA Triton serving.
02
Golf Vision AI turns user-recorded swing video into structured technique analysis and personalized feedback through a deployed backend.
The AI/video-analysis backend and integration were built as part of this work. The iOS application itself was not built as part of this scope.
Tools and methods
03
Work with Mknoon AI spans medical-image model development, evaluation, APIs, deployment, and product integration.
Current benchmarks: 95.5% overall F1 for chest X-ray classification; 85% F1 for dual-view mammography classification; 65.1% mAP for tumor localization.
Automate visual checks and surface cases that require human attention.
Detect and analyze objects or regions across large imagery collections.
Estimate pose, detect events, analyze technique, and provide structured feedback from video.
Develop and evaluate assistive image-analysis models with appropriate domain validation and human oversight.
Automatically categorize, route, or search large image collections.
Build specialized systems where business logic depends on information contained in visual data.
Honest advice
Computer vision is appropriate when the information needed by the workflow genuinely exists in images or video and can be defined clearly enough to evaluate.
Some problems can instead be solved more reliably through metadata, sensors, structured data, or changes to the upstream process.
We start with the workflow and the available signal before deciding what model to train.
How we work
We clarify what must be detected, classified, measured, or understood and how the output will be used.
We analyze dataset quality, labels, edge cases, class distribution, and the conditions the production system will encounter.
We define metrics and evaluation slices that represent real success rather than relying only on one aggregate score.
We train and compare approaches, analyze failures, improve the data, and iterate against the benchmark.
We measure the exported model on the target environment and integrate it into the production workflow.
We clarify what must be detected, classified, measured, or understood and how the output will be used.
We analyze dataset quality, labels, edge cases, class distribution, and the conditions the production system will encounter.
We define metrics and evaluation slices that represent real success rather than relying only on one aggregate score.
We train and compare approaches, analyze failures, improve the data, and iterate against the benchmark.
We measure the exported model on the target environment and integrate it into the production workflow.
Next step
Book a 30-minute conversation with Relytic to discuss the visual data, the decision you need the system to make, and what reliable performance would look like.