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 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 # 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: # 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: # 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 # 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 # 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 # 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: # 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 # 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 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 # 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 # 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 # 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 # Generate augmentation config python scripts/dataset_pipeline_builder.py data/coco/ \ --augment \ --aug-config configs/augmentation.yaml \ --output data/augmented/ Recommended augmentations for detection: # 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 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 # 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 |