Computer Vision YOLO Team Productivity

Computer Vision YOLO Productivity

Computer Vision YOLO Team Productivity Object Detection Real-time Automation Inventory Quality Safety People Counting Production Deploy

YOLO Quick Start

# === YOLO Quick Start ===

# pip install ultralytics
#
# from ultralytics import YOLO
#
# # Load pretrained model
# model = YOLO('yolov8n.pt')  # nano (fastest)
# # model = YOLO('yolov8s.pt')  # small
# # model = YOLO('yolov8m.pt')  # medium
#
# # Inference on image
# results = model('image.jpg')
# results[0].show()  # Display results
# results[0].save('output.jpg')  # Save results
#
# # Inference on video
# results = model('video.mp4', stream=True)
# for r in results:
#     boxes = r.boxes  # Bounding boxes
#     for box in boxes:
#         cls = int(box.cls[0])
#         conf = float(box.conf[0])
#         print(f"Class: {model.names[cls]} Conf: {conf:.2f}")
#
# # Webcam real-time
# results = model(source=0, show=True, stream=True)
# for r in results:
#     pass  # Process frames
#
# # Train custom model
# model = YOLO('yolov8n.pt')
# model.train(data='dataset.yaml', epochs=100, imgsz=640, batch=16)
# # dataset.yaml:
# # path: ./dataset
# # train: images/train
# # val: images/val
# # names: {0: 'helmet', 1: 'no_helmet', 2: 'vest', 3: 'no_vest'}

from dataclasses import dataclass

@dataclass
class YOLOModel:
    name: str
    params: str
    map50: str
    speed_gpu: str
    use_case: str

models = [
    YOLOModel("YOLOv8n (Nano)",
        "3.2M params",
        "mAP50: 37.3%",
        "1.2ms (A100 GPU)",
        "Edge Mobile Real-time สำคัญ"),
    YOLOModel("YOLOv8s (Small)",
        "11.2M params",
        "mAP50: 44.9%",
        "1.5ms",
        "สมดุล Speed/Accuracy"),
    YOLOModel("YOLOv8m (Medium)",
        "25.9M params",
        "mAP50: 50.2%",
        "2.7ms",
        "แม่นกว่า GPU พอ"),
    YOLOModel("YOLOv8l (Large)",
        "43.7M params",
        "mAP50: 52.9%",
        "3.9ms",
        "ต้องการความแม่นยำสูง"),
    YOLOModel("YOLOv8x (XLarge)",
        "68.2M params",
        "mAP50: 53.9%",
        "5.6ms",
        "แม่นสุด ไม่กังวล Speed"),
]

print("=== YOLO Models ===")
for m in models:
    print(f"  [{m.name}] {m.params}")
    print(f"    Accuracy: {m.map50} | Speed: {m.speed_gpu}")
    print(f"    Use: {m.use_case}")

Team Automation

# === Team Productivity Automation ===

@dataclass
class AutomationUseCase:
    use_case: str
    yolo_task: str
    dataset: str
    roi: str
    team_impact: str

automations = [
    AutomationUseCase("Inventory Counting",
        "Detection (นับ Bounding Box ต่อ Class)",
        "ถ่ายภาพสินค้า 500+ ภาพ Label ด้วย Roboflow",
        "ลดเวลา 96% คืนทุน 2 เดือน",
        "Warehouse Team ลดงาน Manual นับของ 2 ชม./วัน"),
    AutomationUseCase("Quality Inspection",
        "Detection + Segmentation (ตรวจ Defect)",
        "ภาพ OK/NG 1000+ ภาพ Label Defect Region",
        "ลด Error 90% Throughput 10x",
        "QC Team ตรวจ 2000+ ชิ้น/ชม. แทน 200 ชิ้น"),
    AutomationUseCase("Safety PPE Detection",
        "Detection (Helmet Vest Glasses Boot)",
        "ภาพคนงาน 2000+ ภาพ Label PPE Items",
        "ลดอุบัติเหตุ 50% ตรวจ 24/7",
        "Safety Team ไม่ต้องตรวจ Manual ได้ Alert Real-time"),
    AutomationUseCase("Meeting Room Occupancy",
        "Detection + Counting (นับคน)",
        "COCO Pretrained (Person Class) ไม่ต้อง Train ใหม่",
        "ประหยัดค่าไฟ HVAC 15-25%",
        "Facility Team ปรับ HVAC อัตโนมัติ ตาม Occupancy"),
    AutomationUseCase("Document Region Detection",
        "Detection (ตรวจ Text Table Image Region)",
        "ภาพเอกสาร 500+ ภาพ Label Region Types",
        "ลดเวลา Data Entry 70%",
        "Admin Team ลดงาน Manual Data Entry"),
]

print("=== Automation Use Cases ===")
for a in automations:
    print(f"\n  [{a.use_case}] Task: {a.yolo_task}")
    print(f"    Dataset: {a.dataset}")
    print(f"    ROI: {a.roi}")
    print(f"    Impact: {a.team_impact}")

Production Deployment

# === Production Deployment ===

# Export for Production
# from ultralytics import YOLO
# model = YOLO('best.pt')  # Your trained model
# model.export(format='engine')  # TensorRT (NVIDIA)
# model.export(format='onnx')    # ONNX (Cross-platform)
# model.export(format='coreml')  # CoreML (Apple)
# model.export(format='tflite')  # TFLite (Mobile/Edge)

# Docker Deployment
# docker run --gpus all -v ./models:/models -p 8000:8000 \
#   ultralytics/ultralytics python serve.py

@dataclass
class DeployOption:
    platform: str
    technology: str
    fps: str
    cost: str
    best_for: str

options = [
    DeployOption("Docker + GPU Server",
        "Docker + NVIDIA GPU + TensorRT",
        "60-100+ FPS",
        "Server Cost (GPU $500-2000/เดือน)",
        "Production Server Multi-camera High FPS"),
    DeployOption("Kubernetes + Triton",
        "K8s + Triton Inference Server + GPU Pool",
        "Scalable (100+ FPS per Pod)",
        "K8s Cluster + GPU Nodes",
        "Enterprise Scale Multi-model Multi-tenant"),
    DeployOption("Edge (Jetson)",
        "NVIDIA Jetson Orin Nano + TensorRT",
        "30-60 FPS (YOLOv8n)",
        "Hardware $5,000-15,000 บาท/ตัว",
        "Factory Floor Retail Store Edge Processing"),
    DeployOption("Cloud (SageMaker)",
        "AWS SageMaker / GCP Vertex AI",
        "Auto-scale",
        "Pay-per-use (g4dn.xlarge $0.526/hr)",
        "Variable Load Batch Processing API Service"),
    DeployOption("Browser (ONNX.js)",
        "ONNX Runtime Web / TensorFlow.js",
        "5-15 FPS (CPU) 30+ FPS (WebGPU)",
        "ฟรี (Client-side)",
        "Demo Prototype Low-volume Client-side"),
]

print("=== Deployment Options ===")
for o in options:
    print(f"\n  [{o.platform}] {o.technology}")
    print(f"    FPS: {o.fps} | Cost: {o.cost}")
    print(f"    Best for: {o.best_for}")

เคล็ดลับ

• YOLOv8n: เริ่มด้วย Nano Model เร็วสุด ถ้าไม่แม่นค่อยเพิ่มเป็น Small/Medium
• Roboflow: ใช้ Roboflow Label + Augment Dataset ง่ายและเร็ว
• TensorRT: Export เป็น TensorRT เร็วขึ้น 2-5 เท่าบน NVIDIA GPU
• Pretrained: COCO Pretrained มี 80 Classes ใช้ได้เลย ไม่ต้อง Train
• ROI: คำนวณ ROI ก่อนเริ่ม Project เพื่อ Justify Budget

YOLO คืออะไร

Real-time Object Detection YOLOv8 Ultralytics Detect Segment Classify Pose Nano Small Medium Large 30-100+ FPS GPU mAP

สรุป

Computer Vision YOLO Team Productivity Object Detection Automation Inventory Quality Safety Deploy TensorRT Docker Kubernetes Edge ROI