Computer Vision YOLO Scaling Strategy — วิธี

YOLO Object Detection

YOLO You Only Look Once Real-time Object Detection ตรวจจับวัตถุครั้งเดียว YOLOv8 Ultralytics Detection Segmentation Pose Estimation CCTV Autonomous Driving

เนื้อหาเกี่ยวข้อง — ดูเพิ่มเติมเรื่อง Apache Kafka Streams Observability Stack — คู่มือฉบับสมบูรณ์ 2026

Scale ได้หลายระดับ Model Nano Small Medium Large GPU TensorRT Kubernetes Batch Processing Edge Device Jetson Raspberry Pi

เนื้อหาเกี่ยวข้อง — บทความที่เกี่ยวข้อง: Certificate Manager DevSecOps Integration

Model	Parameters	mAP	Speed (ms)	เหมาะกับ
YOLOv8n	3.2M	37.3	1.2	Edge / Mobile
YOLOv8s	11.2M	44.9	2.1	Edge / Light Server
YOLOv8m	25.9M	50.2	4.7	Server
YOLOv8l	43.7M	52.9	7.1	Server GPU
YOLOv8x	68.2M	53.9	10.8	High Accuracy

Performance Optimization

# optimization.py — YOLO Performance Optimization

optimizations = {

    "TensorRT FP16": {

        "speedup": "2-3x faster",

        "accuracy_loss": "< 0.5% mAP",

        "command": "yolo export model=yolov8m.pt format=engine half=True",

        "requirement": "NVIDIA GPU (Compute Capability >= 7.0)",

    },

    "TensorRT INT8": {

        "speedup": "3-5x faster",

        "accuracy_loss": "1-2% mAP",

        "command": "yolo export model=yolov8m.pt format=engine int8=True data=coco.yaml",

        "requirement": "Calibration Dataset needed",

    },

    "ONNX Runtime": {

        "speedup": "1.5-2x faster (CPU)",

        "accuracy_loss": "0%",

        "command": "yolo export model=yolov8m.pt format=onnx simplify=True",

        "requirement": "onnxruntime / onnxruntime-gpu",

    },

    "OpenVINO": {

        "speedup": "2-3x faster (Intel CPU)",

        "accuracy_loss": "< 0.5%",

        "command": "yolo export model=yolov8m.pt format=openvino half=True",

        "requirement": "Intel CPU/iGPU",

    },

    "Image Resize": {

        "speedup": "640->320 = 2-4x faster",

        "accuracy_loss": "5-10% mAP",

        "command": "model.predict(source, imgsz=320)",

        "requirement": "ลด Input Resolution",

    },

    "Batch Inference": {

        "speedup": "2-4x throughput",

        "accuracy_loss": "0%",

        "command": "model.predict(sources, batch=16)",

        "requirement": "GPU Memory เพียงพอ",

    },

}



print("YOLO Performance Optimizations:")

for name, info in optimizations.items():

    print(f"\n  [{name}]")

    print(f"    Speedup: {info['speedup']}")

    print(f"    Accuracy Loss: {info['accuracy_loss']}")

    print(f"    Command: {info['command']}")

    print(f"    Requirement: {info['requirement']}")



# GPU Comparison

gpus = {

    "Jetson Nano": {"VRAM": "4GB", "YOLOv8n": "15 FPS", "cost": "$200"},

    "Jetson Orin": {"VRAM": "8-32GB", "YOLOv8n": "60 FPS", "cost": "$500-2000"},

    "T4": {"VRAM": "16GB", "YOLOv8m": "80 FPS", "cost": "$0.5/hr (Cloud)"},

    "A10G": {"VRAM": "24GB", "YOLOv8m": "150 FPS", "cost": "$1/hr (Cloud)"},

    "RTX 4090": {"VRAM": "24GB", "YOLOv8m": "200 FPS", "cost": "$1,599"},

    "A100": {"VRAM": "80GB", "YOLOv8l": "250 FPS", "cost": "$3/hr (Cloud)"},

}



print(f"\n\nGPU Comparison for YOLO:")

for gpu, info in gpus.items():

    print(f"  {gpu:<14} VRAM: {info['VRAM']:<8} Speed: {info.get('YOLOv8m', info.get('YOLOv8n', info.get('YOLOv8l', 'N/A'))):<10} Cost: {info['cost']}")

เคล็ดลับ

TensorRT: Export YOLO เป็น TensorRT FP16 เร็วขึ้น 2-3 เท่า
Model Size: เลือกขนาดตามงาน Nano สำหรับ Edge Large สำหรับ Server
Batch: ใช้ Batch Inference เพิ่ม Throughput 2-4 เท่า
Queue: ใช้ Redis/Kafka Buffer Requests ป้องกัน Overload
HPA: ใช้ Kubernetes HPA Scale Pods ตาม GPU Utilization
Monitor: ติดตาม FPS Latency GPU Memory ด้วย Prometheus

YOLO คืออะไร

You Only Look Once Real-time Object Detection ตรวจจับวัตถุครั้งเดียว YOLOv8 Ultralytics Detection Segmentation Pose CCTV Autonomous Driving Quality Inspection

แนะนำเพิ่มเติม — ติดตาม XM Signal

เนื้อหาเกี่ยวข้อง — ทำความเข้าใจ Grafana Mimir Metrics MLOps Workflow