Derivative-free optimization is required, whenever gradients of objective functions are not available or too expensive to evaluate. A typical imaging application, where such a situation arises, is when employing a closed-box classification tool, like a neural network, where only the model output is available. Consensus-based techniques utilize an ensemble of …

A GUI-based Python framework for segmentation, tracking, cell cycle annotations and quantification of microscopy data.
 
Let's face it, when dealing with segmentation of microscopy data we often do not have time to check that everything is correct, because it is a tedious and very time consuming process. Cell-ACDC comes …

To train deep learning-based segmentation models, large ground truth datasets are needed. To address this need in microfluidic live-cell imaging, we developed CellSium, a flexibly configurable cell simulator built to synthesize realistic image sequences of bacterial microcolonies growing in monolayers. Simulated images are suitable for training neural networks. Generation of …

CellSketch is used to collect masks and labels belonging to the same 3D scene into one project. The routines are primarily targeting but not limited to single cell datasets. All imported datasets are stored in the N5 format as blocks. They can be displayed with BigDataViewer. All labels imported as …

challengeR is a tool for analyzing and visualizing challenge results in the field of image analysis and beyond.

Challenges have become the de facto standard for benchmarking image analysis algorithms. While the number of challenges is steadily increasing, surprisingly little effort has been invested in ensuring high quality design, execution …

CIDS is a framework for Artificial Intelligence (AI) and Machine Learning (ML) for engineering, materials, and natural sciences applications. It combines models, functions, and pipelines from libraries such as tensorflow/keras, sklearn, scipy, and pandas to build modular, flexible, and reproducible AI models.

The interface KadiAI integrates AI tools, such as …

Generalized-YOLOv5 is a modified version of YOLOv5. YOLOv5 itself is a realtime object detection framework designed for natural images. Our Generalized-YOLOv5 version has two crucial extensions. First, an extension that generalizes YOLOv5 also to non-natural images, which enables the usage of state-of-the-art realtime object detection to many domains (e.g. the …

This solution contains a framework for training deep learning-based classification and regression models with Pytorch Lightning. It comprises several architectures, regularization, augmentation and training techniques and aims to provide an easy-to-use suite for experimenting and fast model development using your own datasets. Additionally, extensive experiments on the CIFAR10 dataset provide …

This repository contains an easy-to-use and flexibly customizable framework for developing and training semantic segmentation models. The focus was put on being usable out-of-the-box, without being a black box and giving the possibility to be adapted to individual projects. Therefore several features like pre-trained state-of-the-art models are already provided for …

CrystFEL is a suite of programs for processing serial crystallography data. CrystFEL comprises programs for indexing and integrating diffraction patterns, scaling and merging intensities, simulating patterns, calculating figures of merit for the data and visualising the results.