Image augmentation for machine learning experiments.
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Updated
Apr 6, 2024 - Python
Image augmentation for machine learning experiments.
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Image augmentation for object detection, segmentation and classification
Artificial Intelligence Learning Notes.
AutoML for image augmentation. AutoAlbument uses the Faster AutoAugment algorithm to find optimal augmentation policies. Documentation - https://albumentations.ai/docs/autoalbument/
A simpler way of reading and augmenting image segmentation data into TensorFlow
Image data augmentation on-the-fly by add new class on transforms in PyTorch and torchvision.
Deep Learning for Automatic Pneumonia Detection, RSNA challenge
MemeGen is a web application where the user gives an image as input and our tool generates a meme at one click for the user.
Custom image data generator for TF Keras that supports the modern augmentation module albumentations
Run 3 scripts to (1) Synthesize images (by putting few template images onto backgrounds), (2) Train YOLOv3, and (3) Detect objects for: one image, images, video, webcam, or ROS topic.
Methods for alignment of global image statistics aimed at unsupervised Domain Adaptation and Data Augmentation
Pytorch implements yolov3.Good performance, easy to use, fast speed.
Image augmentation with simultaneous transformation of keypoints, bounding boxes, and segmentation mask
HistoClean is a tool for the preprocessing and augmentation of images used in deep learning models. This easy to use application brings together the most popular image processing packages from across the python universe, meaning no more looking at documentation! HistoClean provides real time feedback to augmentations and preprocessing options. T…
TensorFlow2+ graph image augmentation library optimized for tf.data.Dataset.
discolight is a robust, flexible and infinitely hackable library for generating image augmentations ✨
This repository is containing an object classification & localization project for SINGLE object.
Optimize RandAugment with differentiable operations
Chechink the performance of different augmentation techniques on the BraTS 2020 data.
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