Production Computer Vision with YOLO v11: Object Detection at Scale
Training, optimization, edge deployment, and real-time video processing with YOLO
Production Computer Vision with YOLO v11: Object Detection at Scale
Training, optimization, edge deployment, and real-time video processing with YOLO
Build production computer vision systems using YOLO v11 for object detection, including custom training, model optimization with TensorRT, edge deployment, and real-time video stream processing.
YOLO v11 represents state-of-the-art real-time object detection. Custom training: prepare dataset in YOLO format (images + .txt annotations), train: yolo train model=yolo11m.pt data=dataset.yaml epochs=100 imgsz=640 batch=16. Monitor with wandb integration. Data augmentation with Albumentations: random horizontal flip, color jitter, mosaic, copy-paste for small object handling. Transfer learning: start from pretrained COCO weights (80 classes), fine-tune on custom classes. Achieves excellent performance with just 500-1000 images per class. Optimization: export to TensorRT: yolo export model=best.pt format=engine device=0 half=True. 3-5x inference speedup on NVIDIA GPUs. For CPU: export to OpenVINO or ONNX with INT8 quantization. Edge deployment: YOLO Nano for mobile (<3ms inference on iPhone 15 Pro), TFLite export for Android. Real-time video: OpenCV VideoCapture + YOLO inference in separate threads, use asyncio for handling multiple camera streams. Production architecture: RTSP streams -> Kafka -> Flink for video frame sampling -> YOLO inference workers -> results database + real-time alerting. Typical use cases: manufacturing quality control (defect detection), retail analytics (customer counting, shelf availability), security (intrusion detection, PPE compliance).
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