- Getting started
- Introduction
- Overview of topics
- How to contribute
- General best practices
- Model families
- Classifier / Tagger
- Semantic Segmentor
- Object Detector
- Instance Segmentor
- Attributor
- Model architectures
- ResNet
- Faster R-CNN
- Mask R-CNN
- DeepLabv3+
- U-Net
- FBNetV3
- U-Net++
- Efficient Net
- PAN
- PSPNet
- LinkNet
- FPN
- RetinaNet
- Cascade RCNN
- FBNetV3IS
- FBNetV3OD
- CascadeMask R-CNN
- Metrics
- Confusion Matrix
- Intersection over Union (IoU)
- Accuracy
- Hamming score
- Precision
- Recall
- Precision-Recall curve and AUC-PR
- F-beta score
- Average Precision
- mean Average Precision (mAP)
- Loss
- Cross-Entropy Loss
- Binary Cross-Entropy Loss
- Focal loss
- Bounding Box Regression Loss
- Solvers / Optimizers
- Epsilon Coefficient
- Momentum (SGD)
- Adam
- Adadelta
- Adagrad
- AdaMax
- Adamw
- ASGD
- Base Learning Rate
- RMSprop
- SGD
- Weight Decay
- Training Parameters
- Patience
- Min delta
- Seed
- Batch Size
- Iterations
- Epoch
- Scheduler
- Warm-up
- CyclicLR
- MultiStepLR
- StepLR
- CosineAnnealingLR
- ReduceLROnPlateau
- ExponentialLR
- Augmentations
- Horizontal Flip
- Vertical Flip
- Random Crop
- Random Sized Crop
- Rotate
- Resize
- Blur
- Smallest max size
- Center Crop
- Color Jitter
- Gaussian Noise
- Shift Scale Rotate
- Longest Max Size
- Deployment
- Primitive deployment using web frameworks
- Commonly used web frameworks
- Containerized Deployment
- Orchestrated Deployment
- Challenges of Deployment
- Splits
- General Information
- Random
- Stratification
- MP Wiki
- Augmentations
- Vertical Flip
Vertical Flip
This data augmentation allows the user to vertically flip images with a certain probability.
Parameters
Probability
This produces the probability of vertically flipping each image.
If a large fraction of images needs to be flipped, set a high probability.
Code Implementation
import albumentations as albu
from PIL import Image
import numpy as np
transform =albu.VerticalFlip(p=0.5)
image = np.array(Image.open('/some/random/image.png'))
augmented_image = transform(image=image)['image']
# we have our required flipped image in augmented_image.
Further Resources
Last updated on Jun 01, 2022