domains / software-engineering / computer-vision-engineering / senior-computer-vision

senior-computer-vision

Skill card

combined

The two copies overlap in scope (object detection, segmentation, PyTorch, production deployment) but each contains material steps the other lacks. The first copy is a broad senior-engineering persona with generic MLOps/security/leadership guidance and placeholder scripts. The second copy is a concrete CV engineering skill with specific architectures (YOLO, Faster R-CNN, DETR, SAM), a step-by-step detection pipeline, dataset preparation commands, evaluation metrics, and an optimization/deployment workflow. Crowning one would hide the other's real alternative content.

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Other copies

Combined skill

---
name: senior-computer-vision
description: Computer vision engineering skill for object detection, segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.
---

# Senior Computer Vision Engineer

Production computer vision engineering skill for object detection, image segmentation, and visual AI system deployment.

## Quick Start

```bash
# Generate training configuration for YOLO or Faster R-CNN
python scripts/vision_model_trainer.py models/ --task detection --arch yolov8

# Analyze model for optimization opportunities (quantization, pruning)
python scripts/inference_optimizer.py model.pt --target onnx --benchmark

# Build dataset pipeline with augmentations
python scripts/dataset_pipeline_builder.py images/ --format coco --augment
```

## Core Expertise

- **Object Detection**: YOLO family (v5-v11), Faster R-CNN, DETR, RT-DETR
- **Instance Segmentation**: Mask R-CNN, YOLACT, SOLOv2
- **Semantic Segmentation**: DeepLabV3+, SegFormer, SAM (Segment Anything)
- **Image Classification**: ResNet, EfficientNet, Vision Transformers (ViT, DeiT)
- **Video Analysis**: Object tracking (ByteTrack, SORT), action recognition
- **3D Vision**: Depth estimation, point cloud processing, NeRF
- **Production Deployment**: ONNX, TensorRT, OpenVINO, CoreML
- **MLOps and DataOps**: Scalable system design, performance optimization, model monitoring, security and compliance, cost optimization

## Tech Stack

| Category | Technologies |
|----------|--------------|
| Frameworks | PyTorch, torchvision, timm |
| Detection | Ultralytics (YOLO), Detectron2, MMDetection |
| Segmentation | segment-anything, mmsegmentation |
| Optimization | ONNX, TensorRT, OpenVINO, torch.compile |
| Image Processing | OpenCV, Pillow, albumentations |
| Annotation | CVAT, Label Studio, Roboflow |
| Experiment Tracking | MLflow, Weights & Biases |
| Serving | Triton Inference Server, TorchServe |
| Deployment | Docker, Kubernetes, AWS/GCP/Azure |
| Databases | PostgreSQL, BigQuery, Snowflake, Pinecone |

## Workflow 1: Object Detection Pipeline

### Step 1: Define Detection Requirements

Analyze the detection task requirements:

```
Detection Requirements Analysis:
- Target objects: [list specific classes to detect]
- Real-time requirement: [yes/no, target FPS]
- Accuracy priority: [speed vs accuracy trade-off]
- Deployment target: [cloud GPU, edge device, mobile]
- Dataset size: [number of images, annotations per class]
```

### Step 2: Select Detection Architecture

| Requirement | Recommended Architecture | Why |
|-------------|-------------------------|-----|
| Real-time (>30 FPS) | YOLOv8/v11, RT-DETR | Single-stage, optimized for speed |
| High accuracy | Faster R-CNN, DINO | Two-stage, better localization |
| Small objects | YOLO + SAHI, Faster R-CNN + FPN | Multi-scale detection |
| Edge deployment | YOLOv8n, MobileNetV3-SSD | Lightweight architectures |
| Transformer-based | DETR, DINO, RT-DETR | End-to-end, no NMS required |

### Step 3: Prepare Dataset

```bash
# COCO format (recommended)
python scripts/dataset_pipeline_builder.py data/images/ \
    --annotations data/labels/ \
    --format coco \
    --split 0.8 0.1 0.1 \
    --output data/coco/

# Verify dataset
python -c "from pycocotools.coco import COCO; coco = COCO('data/coco/train.json'); print(f'Images: {len(coco.imgs)}, Categories: {len(coco.cats)}')"
```

### Step 4: Configure Training

```bash
# For Ultralytics YOLO
python scripts/vision_model_trainer.py data/coco/ \
    --task detection \
    --arch yolov8m \
    --epochs 100 \
    --batch 16 \
    --imgsz 640 \
    --output configs/

# For Detectron2
python scripts/vision_model_trainer.py data/coco/ \
    --task detection \
    --arch faster_rcnn_R_50_FPN \
    --framework detectron2 \
    --output configs/
```

### Step 5: Train and Validate

```bash
# Ultralytics training
yolo detect train data=data.yaml model=yolov8m.pt epochs=100 imgsz=640

# Detectron2 training
python train_net.py --config-file configs/faster_rcnn.yaml --num-gpus 1

# Validate on test set
yolo detect val model=runs/detect/train/weights/best.pt data=data.yaml
```

### Step 6: Evaluate Results

| Metric | Target | Description |
|--------|--------|-------------|
| mAP@50 | >0.7 | Mean Average Precision at IoU 0.5 |
| mAP@50:95 | >0.5 | COCO primary metric |
| Precision | >0.8 | Low false positives |
| Recall | >0.8 | Low missed detections |
| Inference time | <33ms | For 30 FPS real-time |

## Workflow 2: Model Optimization and Deployment

### Step 1: Benchmark Baseline Performance

```bash
python scripts/inference_optimizer.py model.pt \
    --benchmark \
    --input-size 640 640 \
    --batch-sizes 1 4 8 16 \
    --warmup 10 \
    --iterations 100
```

### Step 2: Select Optimization Strategy

| Deployment Target | Optimization Path |
|-------------------|-------------------|
| Cloud GPU | TensorRT, ONNX Runtime, batching |
| Edge device | Quantization, pruning, OpenVINO |
| Mobile | CoreML, TFLite, model distillation |

## Production Patterns

### Scalable Data Processing
- Horizontal scaling architecture
- Fault-tolerant design
- Real-time and batch processing
- Data quality validation
- Performance monitoring

### ML Model Deployment
- Model serving with low latency
- A/B testing infrastructure
- Feature store integration
- Model monitoring and drift detection
- Automated retraining pipelines

### Real-Time Inference
- Batching and caching strategies
- Load balancing
- Auto-scaling
- Latency optimization
- Cost optimization

## Best Practices

### Development
- Test-driven development
- Code reviews and pair programming
- Documentation as code
- Version control everything
- Continuous integration

### Production
- Monitor everything critical
- Automate deployments
- Feature flags for releases
- Canary deployments
- Comprehensive logging

### Team Leadership
- Mentor junior engineers
- Drive technical decisions
- Establish coding standards
- Foster learning culture
- Cross-functional collaboration

## Performance Targets

**Latency:**
- P50: < 50ms
- P95: < 100ms
- P99: < 200ms

**Throughput:**
- Requests/second: > 1000
- Concurrent users: > 10,000

**Availability:**
- Uptime: 99.9%
- Error rate: < 0.1%

## Security & Compliance

- Authentication & authorization
- Data encryption (at rest & in transit)
- PII handling and anonymization
- GDPR/CCPA compliance
- Regular security audits
- Vulnerability management

## Common Commands

```bash
# Development
python -m pytest tests/ -v --cov
python -m black src/
python -m pylint src/

# Training
python scripts/train.py --config prod.yaml
python scripts/evaluate.py --model best.pth

# Deployment
docker build -t service:v1 .
kubectl apply -f k8s/
helm upgrade service ./charts/

# Monitoring
kubectl logs -f deployment/service
python scripts/health_check.py
```

## Reference Documentation

- `references/computer_vision_architectures.md`: Advanced patterns, production implementation, performance optimization, scalability, security, case studies
- `references/object_detection_optimization.md`: Step-by-step processes, architecture design, tool integration, performance tuning, troubleshooting
- `references/production_vision_systems.md`: System design, implementation examples, configuration, deployment, monitoring

## Senior-Level Responsibilities

1. **Technical Leadership**: Drive architectural decisions, mentor team members, establish best practices, ensure code quality
2. **Strategic Thinking**: Align with business goals, evaluate trade-offs, plan for scale, manage technical debt
3. **Collaboration**: Work across teams, communicate effectively, build consensus, share knowledge
4. **Innovation**: Stay current with research, experiment with new approaches, contribute to community, drive continuous improvement
5. **Production Excellence**: Ensure high availability, monitor proactively, optimize performance, respond to incidents