Data Augmentation
Showing 20 of 20 projects
A fast and flexible Python library for image augmentation in computer vision tasks like classification, segmentation, and object detection.
A Python library for augmenting images and associated data (heatmaps, keypoints, bounding boxes) for machine learning projects.
A differentiable computer vision library for PyTorch, providing geometric vision and image processing algorithms for AI workflows.
A Python library for programmatically building and managing training data using weak supervision.
A Python library for image augmentation in machine learning, offering a stochastic pipeline approach with fine-grained control over operations.
A deep learning framework for training image classification models to solve complex captcha and OCR tasks.
A Python library for audio data augmentation to improve the robustness of audio machine learning models.
A Python package for generating synthetic tabular and time-series data using state-of-the-art generative models like GANs and Gaussian Mixtures.
A library of modular computer vision components built on Keras 3, supporting TensorFlow, JAX, and PyTorch backends.
A CNN-based captcha solver for Taiwan Railway booking website with a training set generator that mimics captcha style and uses data augmentation.
A Python library for generating high-quality synthetic tabular data using GANs, diffusion models, and large language models.
A U-Net implementation for brain tumor segmentation using the BRATS 2017 dataset with data augmentation and dice loss.
Winning solution for the Galaxy Challenge on Kaggle, using convolutional neural networks to classify galaxy morphologies.
A Python library for annotation-aware musical data augmentation to improve statistical model training.
Python library for audio augmentation, generating multiple audio files from a mono source with speed, tone, and amplitude modifications.
A JAX-based library for fast, composable image augmentation with geometric and color transformations.
A collection of easy-to-use machine learning datasets for Torch7 with built-in preprocessing and sampling utilities.
A distributed data stream pipeline for querying, augmenting, and transforming data using Elixir pattern-matching rules.
A deep learning model for predicting cancer drug response using data enhancement and edge-collaborative update strategies.
A Python algorithm for smoothly blending U-Net image segmentation patches using spline interpolation and batch prediction.
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