Computer Vision System Development

Development of Computer Vision Systems

Computer Vision is a field of ML that solves tasks on images and videos: from simple classification to understanding complex scenes. Development of a CV system is not just about choosing a model, but building a complete pipeline: data collection and annotation, training, evaluation on a representative test set, optimization for target hardware, deployment with data drift monitoring.

Typical CV System Stack

A modern CV system is built on three levels: model, inference server, integration layer.

Models (choice depends on the task):

• Classification: EfficientNet-B4/B7, ViT-B/16, ConvNeXt
• Detection: YOLOv8/YOLO11, RT-DETR, DINO
• Segmentation: Segment Anything Model (SAM), Mask R-CNN, YOLOv8-seg
• Generative: Stable Diffusion, DALL-E 3 (for augmentation)

Inference servers:

• NVIDIA Triton Inference Server — for GPU deployment, batching, model ensemble
• TorchServe — for PyTorch models
• ONNX Runtime — for edge/CPU deployment
• TensorFlow Serving — for TF models

Optimization for production:

• TensorRT — acceleration on NVIDIA GPU: 2–5x compared to PyTorch
• ONNX export → quantization INT8 — for CPU or edge devices
• Pruning — removal of insignificant weights with acceptable accuracy loss

# Export YOLOv8 to TensorRT for production
from ultralytics import YOLO

model = YOLO('best.pt')
model.export(format='engine',          # TensorRT engine
             device=0,
             half=True,                # FP16
             dynamic=False,
             imgsz=640,
             batch=8)

Development Pipeline

Stage 1: Problem and Data Analysis Define the task type (classification / detection / segmentation / etc), latency requirements (real-time < 50ms or batch?), target hardware (GPU/CPU/Edge). Audit existing data: quantity, quality, class balance.

Stage 2: Data Engineering Data collection if insufficient. Annotation: CVAT, Label Studio, Roboflow. Augmentation: albumentations (geometric and color transformations), Mosaic for detection. Splitting: stratified train/val/test.

Stage 3: Training and Experiments MLflow for experiment tracking. Transfer learning from COCO/ImageNet pretrained. Hyperparameter search through Optuna or Ray Tune.

Stage 4: Evaluation and Error Analysis Confusion matrix, precision/recall curves, worst cases analysis. For detection: [email protected], [email protected]:0.95. Test on OOD (out-of-distribution) data.

Stage 5: Optimization and Deployment TensorRT/ONNX, profiling through NVIDIA Nsight. Docker container, Kubernetes deployment, A/B testing against baseline.

Data Requirements