Data drift is the biggest enemy of a CV model in production. We encounter it in every second project: a model with mAP 0.95 on validation drops to 0.6 a month after deployment. The cause — changed lighting, camera angle, or a new object class. To prevent this, you need not just a model but a computer vision pipeline with drift monitoring and automatic retraining. That's exactly what we build.
What problems does computer vision solve?
In manufacturing, defect detection catches defects with 99.5% accuracy, saving up to 70% of quality control costs (average annual savings of $50,000 per production line). In retail, product recognition speeds up checkout by 5x. In logistics, object tracking (pallets, boxes) and barcode reading reduce operational expenses by 30%. Each task requires a specific approach to data engineering and architecture selection. For example, real-time detection (30 FPS) needs a lightweight YOLOv8-nano (three times faster than YOLOv8-large), while medical image segmentation requires a heavy U-Net or SAM. The average ROI for a computer vision system is 6–12 months, thanks to reduced quality control costs and fewer downtimes.
Typical CV system stack
A modern computer vision system consists of three layers: model, inference server, integration layer.
Models (selection 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
Production optimization:
- TensorRT — acceleration on NVIDIA GPUs: 2–5x over PyTorch
- ONNX export -> quantization INT8 — for CPU or edge devices
- Pruning — removing insignificant weights with acceptable accuracy loss
NVIDIA TensorRT documentation confirms that INT8 quantization reduces model size by 75% and accelerates inference on CPU up to 3x.
How to optimize a model for deployment?
- Profile the model using NVIDIA Nsight to identify bottlenecks.
- Convert to TensorRT engine with FP16 or INT8 quantization.
- Configure dynamic batching (e.g., batch=8).
- Deploy in Triton Inference Server with graceful shutdown and A/B testing.
- Enable data drift monitoring — if the distribution changes, the model retrains automatically.
Example of exporting YOLOv8 to TensorRT
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)
For production, not only accuracy but also speed matters. TensorRT with FP16 gives a 2–5x speedup without significant metric loss. After deployment, we enable data drift monitoring — if the distribution changes, the model retrains automatically.
Development pipeline
Stage 1: Task and data analysis Define task type (classification/detection/segmentation/etc.), latency requirements (real-time < 50ms or batch?), target hardware (GPU/CPU/Edge). Audit available data: quantity, quality, class balance.
Stage 2: Data Engineering Data collection if insufficient. Labeling: CVAT, Label Studio, Roboflow. Augmentation: albumentations (geometric and color transforms), Mosaic for detection. Split: stratified train/val/test.
Stage 3: Training and experiments MLflow for experiment tracking. Transfer learning from COCO/ImageNet pretrained. Hyperparameter search via Optuna or Ray Tune.
Stage 4: Evaluation and error analysis Confusion matrix, precision/recall curves, worst-case analysis. For object detection: [email protected], [email protected]:0.95. Test on OOD (out-of-distribution) data.
Stage 5: Optimization and deployment TensorRT/ONNX, profiling via NVIDIA Nsight. Docker container, Kubernetes deployment, A/B test against baseline.
Data requirements
| Task | Minimum | Recommended |
|---|---|---|
| Classification (2–5 classes) | 200 photos/class | 1000+ photos/class |
| Object detection | 500 labeled photos | 2000+ |
| Segmentation | 300 labeled photos | 1500+ |
| Custom OCR | 100 examples/character | 500+ |
| System complexity | Development time |
|---|---|
| Simple classification, ready data | 2–3 weeks |
| Detection/segmentation, data collection | 4–8 weeks |
| Complex system, edge deployment | 8–16 weeks |
What's included in the work
- Pipeline documentation (architecture, training, deployment)
- Training of the client's team on model operation and retraining
- Integration with MLOps tools (MLflow, Kubeflow)
- Data drift monitoring and alerts
- SLA 99.9% inference server uptime
Why choose us
Over 10 years of expertise in Computer Vision, certified engineers (NVIDIA DLI, TensorRT). We have delivered more than 50 projects — from license plate recognition at service stations to satellite image segmentation. Every project includes business KPI measurement: 40% error reduction, 5x process acceleration.
Order a turnkey computer vision system development — we will evaluate your project and offer the optimal solution. Get an engineer consultation for a detailed assessment.