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.
npx skills add majiayu000/claude-skill-registry --skill cv-pipeline-builder --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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, De
- Overview
- CV Tasks Supported
- 1. Image Classification
- 2. Object Detection
- 3. Semantic Segmentation
- Best Practices for CV
- Data Augmentation
- Transfer Learning
- Model Selection
- Integration with SpecWeave
- Commands
What does the cv-pipeline-builder skill do?
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.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill cv-pipeline-builder --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.