Agent skill

cv-pipeline-builder

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.

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/cv-pipeline-builder

SKILL.md

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.

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