cv-pipeline-builder

# 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