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skills/cv-pipeline-builder/SKILL.md

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+---
+name: cv-pipeline-builder
+description: |
+  Computer vision ML pipelines for image classification, object detection, semantic segmentation, and image generation. Activates for "computer vision", "image classification", "object detection", "CNN", "ResNet", "YOLO", "image segmentation", "image preprocessing", "data augmentation". Builds end-to-end CV pipelines with PyTorch/TensorFlow, integrated with SpecWeave increments.
+---
+
+# Computer Vision Pipeline Builder
+
+## Overview
+
+Specialized ML pipelines for computer vision tasks. Handles image preprocessing, data augmentation, CNN architectures, transfer learning, and deployment for production CV systems.
+
+## CV Tasks Supported
+
+### 1. Image Classification
+
+```python
+from specweave import CVPipeline
+
+# Binary or multi-class classification
+pipeline = CVPipeline(
+    task="classification",
+    num_classes=10,
+    increment="0042"
+)
+
+# Automatically configures:
+# - Image preprocessing (resize, normalize)
+# - Data augmentation (rotation, flip, color jitter)
+# - CNN architecture (ResNet, EfficientNet, ViT)
+# - Transfer learning from ImageNet
+# - Training loop with validation
+# - Inference pipeline
+
+pipeline.fit(train_images, train_labels)
+```
+
+### 2. Object Detection
+
+```python
+# Detect multiple objects in images
+pipeline = CVPipeline(
+    task="object_detection",
+    classes=["person", "car", "dog", "cat"],
+    increment="0042"
+)
+
+# Uses: YOLO, Faster R-CNN, or RetinaNet
+# Returns: Bounding boxes + class labels + confidence scores
+```
+
+### 3. Semantic Segmentation
+
+```python
+# Pixel-level classification
+pipeline = CVPipeline(
+    task="segmentation",
+    num_classes=21,
+    increment="0042"
+)
+
+# Uses: U-Net, DeepLab, or SegFormer
+# Returns: Segmentation mask for each pixel
+```
+
+## Best Practices for CV
+
+### Data Augmentation
+
+```python
+from specweave import ImageAugmentation
+
+aug = ImageAugmentation(increment="0042")
+
+# Standard augmentations
+aug.add_transforms([
+    "random_rotation",  # ±15 degrees
+    "random_flip_horizontal",
+    "random_brightness",  # ±20%
+    "random_contrast",  # ±20%
+    "random_crop"
+])
+
+# Advanced augmentations
+aug.add_advanced([
+    "mixup",  # Mix two images
+    "cutout",  # Random erasing
+    "autoaugment"  # Learned augmentation
+])
+```
+
+### Transfer Learning
+
+```python
+# Start from pre-trained ImageNet models
+pipeline = CVPipeline(task="classification")
+
+# Option 1: Feature extraction (freeze backbone)
+pipeline.use_pretrained(
+    model="resnet50",
+    freeze_backbone=True
+)
+
+# Option 2: Fine-tuning (unfreeze after few epochs)
+pipeline.use_pretrained(
+    model="resnet50",
+    freeze_backbone=False,
+    fine_tune_after_epoch=3
+)
+```
+
+### Model Selection
+
+**Image Classification**:
+- Small datasets (<10K): ResNet18, MobileNetV2
+- Medium datasets (10K-100K): ResNet50, EfficientNet-B0
+- Large datasets (>100K): EfficientNet-B3, Vision Transformer
+
+**Object Detection**:
+- Real-time (>30 FPS): YOLOv8, SSDLite
+- High accuracy: Faster R-CNN, RetinaNet
+
+**Segmentation**:
+- Medical imaging: U-Net
+- Scene segmentation: DeepLabV3, SegFormer
+
+## Integration with SpecWeave
+
+```python
+# CV increment structure
+.specweave/increments/0042-image-classifier/
+├── spec.md
+├── data/
+│   ├── train/
+│   ├── val/
+│   └── test/
+├── models/
+│   ├── model-v1.pth
+│   └── model-v2.pth
+├── experiments/
+│   ├── baseline-resnet18/
+│   ├── resnet50-augmented/
+│   └── efficientnet-b0/
+└── deployment/
+    ├── onnx_model.onnx
+    └── inference.py
+```
+
+## Commands
+
+```bash
+/ml:cv-pipeline --task classification --model resnet50
+/ml:cv-evaluate 0042  # Evaluate on test set
+/ml:cv-deploy 0042    # Export to ONNX
+```
+
+Quick setup for CV projects with production-ready pipelines.