Agent skill · software engineering · borghei
senior-computer-vision
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.
Why this skill is useful
Adds executable scripts for training, optimization, and dataset preparation that extend the AI's capabilities in computer vision workflows.
What it needs
Requires python installed locally. About 3k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
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, confirm these inputs. If any is unknown or vague, ASK — do not assume: [ ] 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 datasetpipelinebuilder.py) [ ] Deployment target — GPU / edge / mobile (drives architecture choice and inferenceoptimizer --target) Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/senior-computer-vision