scikit-image 0.19.1-foss-2021b

There is a newer install of scikit-image

Scikit-learn integrates machine learning algorithms in the tightly-knit scientific Python world, building upon numpy, scipy, and matplotlib. As a machine-learning module, it provides versatile tools for data mining and analysis in any field of science and engineering. It strives to be simple and efficient, accessible to everybody, and reusable in various contexts.

Accessing scikit-image 0.19.1-foss-2021b

To load the module for scikit-image 0.19.1-foss-2021b please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):

📋 module load bear-apps/2021b
module load scikit-image/0.19.1-foss-2021b

BEAR Apps Version




The listed architectures consist of two part: OS-CPU. The OS used is represented by EL and there are several different processor (CPU) types available on BlueBEAR. More information about the processor types on BlueBEAR is available on the BlueBEAR Job Submission page.


  • imread 0.7.4
  • pooch 1.5.2
  • PyWavelets 1.2.0
  • scikit-image-0.19.1
  • tifffile 2021.11.2

More Information

For more information visit the scikit-image website.


This version of scikit-image has a direct dependency on: dask/2022.1.0-foss-2021b foss/2021b imageio/2.13.5-foss-2021b matplotlib/3.4.3-foss-2021b networkx/2.6.3-foss-2021b Pillow/8.3.2-GCCcore-11.2.0 Python/3.9.6-GCCcore-11.2.0

Required By

This version of scikit-image is a direct dependent of: aicsimageio/4.9.4-foss-2021b BEAR-Python-MSc-Bioinformatics/2021b-foss-2021b cuCIM/23.2.0-foss-2021b-CUDA-11.4.1 DeepCell/0.11.1-foss-2021b-CUDA-11.4.1 SSAM/1.0.2-foss-2021b

Other Versions

These versions of scikit-image are available on the BEAR systems (BlueBEAR and BEAR Cloud VMs). These will be retained in accordance with our Applications Support and Retention Policy.

Last modified on 28th February 2023