# Albumentations — Fast Image Augmentation Library for ML Pipelines

> Albumentations is a fast and flexible image augmentation library for machine learning that supports classification, segmentation, and detection tasks with a composable transform API.

## Install

Save as a script file and run:

# Albumentations — Fast Image Augmentation Library for ML Pipelines

## Quick Use
```bash
pip install albumentations
python -c "
import albumentations as A
import numpy as np
transform = A.Compose([A.HorizontalFlip(p=0.5), A.RandomBrightnessContrast(p=0.3)])
image = np.random.randint(0, 256, (256, 256, 3), dtype=np.uint8)
result = transform(image=image)
print('Augmented shape:', result['image'].shape)
"
```

## Introduction
Albumentations provides a fast, composable API for image augmentation in computer vision pipelines. It wraps OpenCV operations in a declarative transform interface and supports pixel-level, spatial, and domain-specific transforms for classification, segmentation, object detection, and keypoint tasks.

## What Albumentations Does
- Offers 70+ augmentation transforms: geometric, color, blur, noise, weather, and more
- Handles bounding boxes, segmentation masks, and keypoints alongside image transforms automatically
- Composes transforms with `Compose`, `OneOf`, `SomeOf`, and `ReplayCompose` for reproducibility
- Runs transforms on NumPy arrays using optimized OpenCV backends for speed
- Integrates with PyTorch, TensorFlow, and other frameworks via simple dataset wrappers

## Architecture Overview
Transforms inherit from `ImageOnlyTransform` or `DualTransform` (the latter also modifies masks/bboxes). `Compose` chains transforms and applies them sequentially, passing an augmented dictionary with keys like `image`, `mask`, `bboxes`, and `keypoints`. Bounding box formats (Pascal VOC, COCO, YOLO, Albumentations) are converted internally via `BboxParams`. Probabilities and parameter ranges are set per transform, giving fine-grained control.

## Self-Hosting & Configuration
- Install via pip: `pip install albumentations`
- Define a pipeline: `A.Compose([A.Resize(224, 224), A.Normalize(), A.pytorch.transforms.ToTensorV2()])`
- Pass bounding box format: `A.Compose([...], bbox_params=A.BboxParams(format='coco'))`
- Save and load pipelines: `A.save(transform, 'pipeline.json')` and `A.load('pipeline.json')`
- Use `ReplayCompose` to record which augmentations were applied for debugging

## Key Features
- Fastest pure-Python augmentation library due to OpenCV-optimized operations
- Unified API for image, mask, bounding box, and keypoint augmentation in a single pass
- Serialization support lets you version-control augmentation pipelines as JSON or YAML
- Large community with 70+ transforms covering standard and creative augmentations
- Battle-tested in Kaggle competitions and production vision pipelines

## Comparison with Similar Tools
- **torchvision.transforms** — PyTorch built-in but slower and lacks native bbox/mask support
- **Kornia** — differentiable augmentations on GPU tensors; Albumentations works on NumPy/CPU
- **imgaug** — similar scope but less actively maintained and generally slower
- **Augmentor** — pipeline-based but narrower transform set
- **NVIDIA DALI** — GPU-accelerated data loading and augmentation; heavier setup

## FAQ
**Q: How does Albumentations handle bounding boxes during spatial transforms?**
A: When you set `bbox_params`, spatial transforms (crop, rotate, flip) automatically adjust bounding box coordinates and clip or remove boxes that fall outside the image.

**Q: Can I use Albumentations with TensorFlow/Keras?**
A: Yes. Apply transforms to NumPy arrays in your data generator or `tf.data` pipeline before converting to tensors.

**Q: Why is Albumentations faster than torchvision transforms?**
A: It uses OpenCV for pixel operations and NumPy for spatial math, which are faster than PIL-based transforms used by torchvision.

**Q: How do I add a custom transform?**
A: Subclass `ImageOnlyTransform` or `DualTransform`, implement `apply()` and optionally `apply_to_mask()`, and use it inside `Compose`.

## Sources
- https://github.com/albumentations-team/albumentations
- https://albumentations.ai/

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Source: https://tokrepo.com/en/workflows/43c2bcef-3e26-11f1-9bc6-00163e2b0d79
Author: Script Depot