Update README.md

README.md

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+license: mit
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-Edit this `README.md` markdown file to author your organization card.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6328b534b0910efc278133ba/iXsFX9ZfyKbn8JSH_ohi_.png)
+
+# Albumentations
+
+Efficient Image Augmentation for Machine Learning in Python
+
+[Albumentations](https://albumentations.ai/) is a fast, flexible image augmentation library designed for machine learning practitioners working on computer vision tasks. Our aim is simple: provide a comprehensive set of tools that can transform any image to augment your datasets, thereby improving model accuracy and robustness.
+
+Features:
+
+- [Wide Range of Augmentations](https://albumentations.ai/docs/api_reference/full_reference/): Supports geometric transforms, color augmentations, flips, rotations, and more, tailored for classification, segmentation, object detection, and working with key points.
+- [Easy Integration](https://albumentations.ai/docs/#examples): Designed to easily fit into any machine learning pipeline.
+- [Performance Optimized](https://albumentations.ai/docs/benchmarking_results/): Minimizes CPU/GPU load with efficient implementation.
+- Community Driven: Open to contributions and feedback. We evolve with your needs.
+
+```python
+from albumentations import (
+    HorizontalFlip, Affine, CLAHE, RandomRotate90,
+    Transpose, ShiftScaleRotate, Blur, OpticalDistortion, GridDistortion, HueSaturationValue,
+    GaussNoise, MotionBlur, MedianBlur,
+    RandomBrightnessContrast, Flip, OneOf, Compose
+)
+import numpy as np
+
+def strong_aug(p=0.5):
+    return Compose([
+        RandomRotate90(),
+        Flip(),
+        Transpose(),
+        GaussNoise(),
+        OneOf([
+            MotionBlur(p=0.2),
+            MedianBlur(blur_limit=3, p=0.1),
+            Blur(blur_limit=3, p=0.1),
+        ], p=0.2),
+        Affine(translate_percent=0.0625, scale=(0.8, 1.2), rotate_limit=(-45, 45), p=0.2),
+        OneOf([
+            OpticalDistortion(p=0.3),
+            GridDistortion(p=0.1)
+        ], p=0.2),
+        OneOf([
+            CLAHE(clip_limit=2),
+            RandomBrightnessContrast(),
+        ], p=0.3),
+        HueSaturationValue(p=0.3),
+    ], p=p)
+
+image = np.ones((300, 300, 3), dtype=np.uint8)
+mask = np.ones((300, 300), dtype=np.uint8)
+whatever_data = "my name"
+augmentation = strong_aug(p=0.9)
+data = {"image": image, "mask": mask, "whatever_data": whatever_data, "additional": "hello"}
+augmented = augmentation(**data)
+image, mask, whatever_data, additional = augmented["image"], augmented["mask"], augmented["whatever_data"], augmented["additional"]
+
+```