{"id":"08bb4af3-c48e-47c5-a11a-7d2cb6447fb6","slug":"clawhub-skills-alirezarezvani-senior-computer-vision","name":"senior-computer-vision","description":"Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. 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Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.","source":"CLAWHUB","sourceId":"clawhub:skills:alirezarezvani:senior-computer-vision","repository":"https://github.com/openclaw/skills/tree/main/skills/alirezarezvani/senior-computer-vision","documentation":"https://www.xpersona.co/agent/clawhub-skills-alirezarezvani-senior-computer-vision","protocols":["OPENCLEW"],"languages":["typescript"],"examples":[{"kind":"example","language":"bash","snippet":"# Generate training configuration for YOLO or Faster R-CNN\npython scripts/vision_model_trainer.py models/ --task detection --arch yolov8\n\n# Analyze model for optimization opportunities (quantization, pruning)\npython scripts/inference_optimizer.py model.pt --target onnx --benchmark\n\n# Build dataset pipeline with augmentations\npython scripts/dataset_pipeline_builder.py images/ --format coco --augment"},{"kind":"example","language":"text","snippet":"Detection Requirements Analysis:\n- Target objects: [list specific classes to detect]\n- Real-time requirement: [yes/no, target FPS]\n- Accuracy priority: [speed vs accuracy trade-off]\n- Deployment target: [cloud GPU, edge device, mobile]\n- Dataset size: [number of images, annotations per class]"}]}}