Editing Computer Vision (section)

== <span style="color: #FFFFFF;">Creating</span> ==
Designing a production computer vision system:

'''1. Define task taxonomy precisely'''
* Classification only? Detection? Segmentation? Pose estimation?
* Single class or multi-class/multi-label?
* Real-time constraint? (determines architecture choice)

'''2. Data pipeline architecture'''
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Raw images
    ↓
[Quality filtering: blur detection, resolution check]
    ↓
[Annotation: LabelStudio / CVAT / Roboflow]
    ↓
[Augmentation: Albumentations pipeline]
    ↓
[Train/Val/Test split: stratified by class]
    ↓
[Model training + experiment tracking]
    ↓
[Evaluation: mAP, confusion matrix, latency]
    ↓
[Deployment: ONNX export → serving]
    ↓
[Monitoring: prediction distribution drift, confidence degradation]
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'''3. Active learning loop''' for continuous improvement:
* Deploy model, collect low-confidence predictions
* Route uncertain samples to human labelers
* Retrain on expanded dataset
* Evaluate and deploy if metrics improve

'''4. Key tooling'''
* Training: PyTorch + Lightning, Ultralytics (YOLO)
* Annotation: Roboflow, CVAT, Label Studio
* Augmentation: Albumentations
* Serving: Triton Inference Server, TorchServe, ONNX Runtime

[[Category:Artificial Intelligence]]
[[Category:Computer Vision]]
[[Category:Deep Learning]]
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