Skill: alirezarezvani/senior-computer-vision (version 2.1.1)

--- 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: { 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


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