Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)

Specification for the bioimage.io model description file.

[NeurIPS 2025] Official code for JAFAR: Jack up Any Feature at Any Resolution

Java bridge to Python-implemented Segment Anything Networks (SAMs)

Segment Anything for Microscopy

A Python library for the Docker Engine API

Comprehensive platform for automated large-scale connectomics. Segmentation and Detection models built upon Funke lab's algorithms.

An ImageJ/Fiji plugin designed to effortlessly integrate Segment-Anything models (SAMs) without code.

Website for the BioImage Model zoo -- a model zoo for bioimage analysis.

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch

A systematic approach to creating better documentation.

BiaPy landing page

BiaPy GUI

BiaPy's project documentation

Open source Python library for building bioimage analysis pipelines

BiaPy's BMZ connection

(deprecated in favor of bioimage-io/collection) RDF collection for BioImage.IO

Interactive image stack viewing in jupyter notebooks based on ipycanvas and ipywidgets

[ICLR'23 Spotlight🔥] The first successful BERT/MAE-style pretraining on any convolutional network; Pytorch impl. of "Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling"

A collection of literature after or concurrent with Masked Autoencoder (MAE) (Kaiming He el al.).

Official Code for DragGAN (SIGGRAPH 2023)

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

The Tensorflow, Keras implementation of U-net, V-net, U-net++, UNET 3+, Attention U-net, R2U-net, ResUnet-a, U^2-Net, TransUNET, and Swin-UNET with optional ImageNet-trained backbones.

Convert Machine Learning Code Between Frameworks

How Distance Transform Maps Boost Segmentation CNNs: An Empirical Study

Fill holes in binary 2D & 3D images fast.

Toolbox for Identifying Mitochondria Instance Segmentation Errors

[pip install medmnist] 18x Standardized Datasets for 2D and 3D Biomedical Image Classification