senior-computer-vision

name: senior-computer-visiondescription: >  Computer vision engineering for object detection, segmentation, and visual AI, covering CNN  and Vision Transformer architectures and ONNX/TensorRT deployment. Use when building  detection pipelines, training models, or optimizing inference.license: MIT + Commons Clausemetadata:   version: 1.1.0  author: borghei  category: engineering  domain: computer-vision  updated: 2026-06-17  tags: [object-detection, image-segmentation, computer-vision, model-training]

Senior Computer Vision Engineer

Design end-to-end computer vision pipelines for object detection, instance/semantic segmentation, and production deployment. Generates training configurations for YOLO/Detectron2/MMDetection, optimizes models for ONNX/TensorRT/OpenVINO runtimes, and builds dataset preparation workflows with format conversion and augmentation.

Core Capabilities

• Detection pipeline design — requirements analysis, architecture selection (YOLO/RT-DETR/Faster R-CNN/DINO), dataset prep, training config, and metric evaluation.
• Model optimization & deployment — baseline benchmarking, ONNX export, INT8/FP16 quantization, and conversion to TensorRT/OpenVINO/CoreML/TFLite per target platform.
• Dataset engineering — audit, cleaning, format conversion (COCO/YOLO/VOC/CVAT/LabelMe), augmentation config, and stratified train/val/test splits.
• Architecture guidance — detection and segmentation architecture trade-offs plus CNN vs Vision Transformer selection.
• Production targets — FPS, mAP, latency P99, memory, and model-size budgets for real-time, high-accuracy, and edge deployments.

When to Use

• Building an object detection or segmentation system from scratch.
• Optimizing and deploying a trained model to GPU, edge, or mobile.
• Preparing, converting, or auditing a computer vision dataset.
• Choosing an architecture for a speed/accuracy/deployment trade-off.

Clarify First

Before generating training configs or pipelines, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• Task: detection / instance or semantic segmentation / classification (selects the architecture and --task).
• Dataset: location and format (COCO / YOLO / VOC) to analyze or convert (the input to dataset_pipeline_builder.py).
• Deployment target: GPU / edge / mobile (drives architecture choice and inference_optimizer --target).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

Tools

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/detection-workflows.md — quick-start commands and the three end-to-end workflows (detection pipeline, model optimization/deployment, dataset prep) plus the architecture selection guide. Read when executing a pipeline.
• references/commands-targets-and-troubleshooting.md — framework command catalogs (YOLO/Detectron2/MMDetection/optimization), performance targets, anti-patterns, troubleshooting table, and success criteria. Read while running training or deployment.
• references/tool-reference.md — full parameter, example, and output-format reference for the three scripts. Read when scripting the tools.
• references/computer_vision_architectures.md — CNN backbones (ResNet, EfficientNet, ConvNeXt), ViT variants (ViT, DeiT, Swin), detection heads, and FPN/BiFPN/PANet necks. Read when choosing or tuning architectures.
• references/object_detection_optimization.md — NMS variants, anchor optimization, loss design (focal, GIoU/CIoU/DIoU), training strategies, and detection augmentation. Read when improving detection accuracy.
• references/production_vision_systems.md — ONNX/TensorRT export, batch inference, edge deployment (Jetson, Intel NCS), Triton serving, and video pipelines. Read when deploying to production.

Scope & Limitations

This skill covers:

• End-to-end object detection and segmentation pipeline design (data preparation through production deployment)
• Training configuration generation for Ultralytics YOLO, Detectron2, and MMDetection frameworks
• Model optimization and export to ONNX, TensorRT, OpenVINO, and CoreML runtimes
• Dataset format conversion (COCO, YOLO, Pascal VOC, CVAT), splitting, validation, and augmentation configuration

This skill does NOT cover:

• Generative vision tasks (image generation, style transfer, super-resolution) -- see dedicated generative AI skills
• 3D reconstruction, SLAM, or point cloud processing beyond basic depth estimation
• Medical imaging regulatory compliance (DICOM, FDA 510(k)) -- see ra-qm-team/ compliance skills
• Real-time video streaming infrastructure (RTSP, WebRTC, GStreamer pipeline design) -- see senior-devops for infrastructure

Integration Points