Senior Computer Vision Engineer

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

Table of Contents

• Quick Start
• Core Expertise
• Tech Stack
• Workflow 1: Object Detection Pipeline
• Workflow 2: Model Optimization and Deployment
• Workflow 3: Custom Dataset Preparation
• Architecture Selection Guide
• Reference Documentation

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

This skill provides guidance on:

• 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

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 |

Workflow 1: Object Detection Pipeline

Use this workflow when building an object detection system from scratch.

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

Choose architecture based on requirements:

| 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

Convert annotations to required format:

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

Generate training configuration:

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

Key metrics to analyze:

| 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

Use this workflow when preparing a trained model for production deployment.

Step 1: Benchmark Baseline Performance

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

Expected output:

Baseline Performance (PyTorch FP32):
- Batch 1: 45.2ms (22.1 FPS)
- Batch 4: 89.4ms (44.7 FPS)
- Batch 8: 165.3ms (48.4 FPS)
- Memory: 2.1 GB
- Parameters: 25.9M

Step 2: Select Optimization Strategy

| Deployment Target | Optimization Path | |-------------------|-------------------| | NVIDIA GPU (cloud) | PyTorch → ONNX → TensorRT FP16 | | NVIDIA GPU (edge) | PyTorch → TensorRT INT8 | | Intel CPU | PyTorch → ONNX → OpenVINO | | Apple Silicon | PyTorch → CoreML | | Generic CPU | PyTorch → ONNX Runtime | | Mobile | PyTorch → TFLite or ONNX Mobile |

Step 3: Export to ONNX

bash
# Export with dynamic batch size
python scripts/inference_optimizer.py model.pt \
    --export onnx \
    --input-size 640 640 \
    --dynamic-batch \
    --simplify \
    --output model.onnx

# Verify ONNX model
python -c "import onnx; model = onnx.load('model.onnx'); onnx.checker.check_model(model); print('ONNX model valid')"

Step 4: Apply Quantization (Optional)

For INT8 quantization with calibration:

bash
# Generate calibration dataset
python scripts/inference_optimizer.py model.onnx \
    --quantize int8 \
    --calibration-data data/calibration/ \
    --calibration-samples 500 \
    --output model_int8.onnx

Quantization impact analysis:

| Precision | Size | Speed | Accuracy Drop | |-----------|------|-------|---------------| | FP32 | 100% | 1x | 0% | | FP16 | 50% | 1.5-2x | <0.5% | | INT8 | 25% | 2-4x | 1-3% |

Step 5: Convert to Target Runtime

bash
# TensorRT (NVIDIA GPU)
trtexec --onnx=model.onnx --saveEngine=model.engine --fp16

# OpenVINO (Intel)
mo --input_model model.onnx --output_dir openvino/

# CoreML (Apple)
python -c "import coremltools as ct; model = ct.convert('model.onnx'); model.save('model.mlpackage')"

Step 6: Benchmark Optimized Model

bash
python scripts/inference_optimizer.py model.engine \
    --benchmark \
    --runtime tensorrt \
    --compare model.pt

Expected speedup:

Optimization Results:
- Original (PyTorch FP32): 45.2ms
- Optimized (TensorRT FP16): 12.8ms
- Speedup: 3.5x
- Accuracy change: -0.3% mAP

Workflow 3: Custom Dataset Preparation

Use this workflow when preparing a computer vision dataset for training.

Step 1: Audit Raw Data

bash
# Analyze image dataset
python scripts/dataset_pipeline_builder.py data/raw/ \
    --analyze \
    --output analysis/

Analysis report includes:

Dataset Analysis:
- Total images: 5,234
- Image sizes: 640x480 to 4096x3072 (variable)
- Formats: JPEG (4,891), PNG (343)
- Corrupted: 12 files
- Duplicates: 45 pairs

Annotation Analysis:
- Format detected: Pascal VOC XML
- Total annotations: 28,456
- Classes: 5 (car, person, bicycle, dog, cat)
- Distribution: car (12,340), person (8,234), bicycle (3,456), dog (2,890), cat (1,536)
- Empty images: 234

Step 2: Clean and Validate

bash
# Remove corrupted and duplicate images
python scripts/dataset_pipeline_builder.py data/raw/ \
    --clean \
    --remove-corrupted \
    --remove-duplicates \
    --output data/cleaned/

Step 3: Convert Annotation Format

bash
# Convert VOC to COCO format
python scripts/dataset_pipeline_builder.py data/cleaned/ \
    --annotations data/annotations/ \
    --input-format voc \
    --output-format coco \
    --output data/coco/

Supported format conversions:

| From | To | |------|-----| | Pascal VOC XML | COCO JSON | | YOLO TXT | COCO JSON | | COCO JSON | YOLO TXT | | LabelMe JSON | COCO JSON | | CVAT XML | COCO JSON |

Step 4: Apply Augmentations

bash
# Generate augmentation config
python scripts/dataset_pipeline_builder.py data/coco/ \
    --augment \
    --aug-config configs/augmentation.yaml \
    --output data/augmented/

Recommended augmentations for detection:

yaml
# configs/augmentation.yaml
augmentations:
  geometric:
    - horizontal_flip: { p: 0.5 }
    - vertical_flip: { p: 0.1 }  # Only if orientation invariant
    - rotate: { limit: 15, p: 0.3 }
    - scale: { scale_limit: 0.2, p: 0.5 }

  color:
    - brightness_contrast: { brightness_limit: 0.2, contrast_limit: 0.2, p: 0.5 }
    - hue_saturation: { hue_shift_limit: 20, sat_shift_limit: 30, p: 0.3 }
    - blur: { blur_limit: 3, p: 0.1 }

  advanced:
    - mosaic: { p: 0.5 }  # YOLO-style mosaic
    - mixup: { p: 0.1 }   # Image mixing
    - cutout: { num_holes: 8, max_h_size: 32, max_w_size: 32, p: 0.3 }

Step 5: Create Train/Val/Test Splits

bash
python scripts/dataset_pipeline_builder.py data/augmented/ \
    --split 0.8 0.1 0.1 \
    --stratify \
    --seed 42 \
    --output data/final/

Split strategy guidelines:

| Dataset Size | Train | Val | Test | |--------------|-------|-----|------| | <1,000 images | 70% | 15% | 15% | | 1,000-10,000 | 80% | 10% | 10% | | >10,000 | 90% | 5% | 5% |

Step 6: Generate Dataset Configuration

bash
# For Ultralytics YOLO
python scripts/dataset_pipeline_builder.py data/final/ \
    --generate-config yolo \
    --output data.yaml

# For Detectron2
python scripts/dataset_pipeline_builder.py data/final/ \
    --generate-config detectron2 \
    --output detectron2_config.py

Architecture Selection Guide

Object Detection Architectures

| Architecture | Speed | Accuracy | Best For | |--------------|-------|----------|----------| | YOLOv8n | 1.2ms | 37.3 mAP | Edge, mobile, real-time | | YOLOv8s | 2.1ms | 44.9 mAP | Balanced speed/accuracy | | YOLOv8m | 4.2ms | 50.2 mAP | General purpose | | YOLOv8l | 6.8ms | 52.9 mAP | High accuracy | | YOLOv8x | 10.1ms | 53.9 mAP | Maximum accuracy | | RT-DETR-L | 5.3ms | 53.0 mAP | Transformer, no NMS | | Faster R-CNN R50 | 46ms | 40.2 mAP | Two-stage, high quality | | DINO-4scale | 85ms | 49.0 mAP | SOTA transformer |

Segmentation Architectures

| Architecture | Type | Speed | Best For | |--------------|------|-------|----------| | YOLOv8-seg | Instance | 4.5ms | Real-time instance seg | | Mask R-CNN | Instance | 67ms | High-quality masks | | SAM | Promptable | 50ms | Zero-shot segmentation | | DeepLabV3+ | Semantic | 25ms | Scene parsing | | SegFormer | Semantic | 15ms | Efficient semantic seg |

CNN vs Vision Transformer Trade-offs

| Aspect | CNN (YOLO, R-CNN) | ViT (DETR, DINO) | |--------|-------------------|------------------| | Training data needed | 1K-10K images | 10K-100K+ images | | Training time | Fast | Slow (needs more epochs) | | Inference speed | Faster | Slower | | Small objects | Good with FPN | Needs multi-scale | | Global context | Limited | Excellent | | Positional encoding | Implicit | Explicit |

Reference Documentation

→ See references/reference-docs-and-commands.md for details

Performance Targets

| Metric | Real-time | High Accuracy | Edge | |--------|-----------|---------------|------| | FPS | >30 | >10 | >15 | | mAP@50 | >0.6 | >0.8 | >0.5 | | Latency P99 | <50ms | <150ms | <100ms | | GPU Memory | <4GB | <8GB | <2GB | | Model Size | <50MB | <200MB | <20MB |

Resources

• Architecture Guide: references/computer_vision_architectures.md
• Optimization Guide: references/object_detection_optimization.md
• Deployment Guide: references/production_vision_systems.md
• Scripts: scripts/ directory for automation tools