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

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

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

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

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

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

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

/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.