senior-computer-vision

---
name: senior-computer-vision
description: Computer vision engineering skill for object detection, image 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.

## Table of Contents

- [Quick Start](#quick-start)
- [Core Expertise](#core-expertise)
- [Tech Stack](#tech-stack)
- [Workflow 1: Object Detection Pipeline](#workflow-1-object-detection-pipeline)
- [Workflow 2: Model Optimization and Deployment](#workflow-2-model-optimization-and-deployment)
- [Workflow 3: Custom Dataset Preparation](#workflow-3-custom-dataset-preparation)
- [Architecture Selection Guide](#architecture-selection-guide)
- [Reference Documentation](#reference-documentation)
- [Common Commands](#common-commands)

## 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: { 

... (truncated)