# I built haralickt: > 20x faster GPU-accelerated Haralick texture features (GLCM) in PyTorch with identical precision to Mahotas

> Source: <https://dev.to/sergeiyvaryukhin/i-built-haralickt-20x-faster-gpu-accelerated-haralick-texture-features-glcm-in-pytorch-with-12ln>
> Published: 2026-11-13 12:14:25+00:00

Hi everyone! 

If you work with texture analysis (medical imaging, satellite data, or industrial defect detection), you've likely noticed that classic libraries like mahotas and scikit-image compute Gray-Level Co-occurrence Matrices (GLCM) and Haralick features strictly on the CPU. On large datasets, this quickly becomes a major pipeline bottleneck.

To solve this, I wrote and open-sourced haralickt — a library that offloads these computations to the GPU using vectorized matrix operations in PyTorch and CUDA.

Key highlights:

• > 20x faster than traditional CPU implementations on high-resolution images and batches.

• Identical numerical precision to classic implementations (verified against mahotas).

• PyTorch-native: works seamlessly with torch.Tensor on GPU without costly CPU-GPU data transfers.

• Open Source (MPL 2.0): free to use in research and commercial projects.

Quick installation:

bash

pip install haralickt

Используйте код с осторожностью.

GitHub repository & benchmarks: [[https://github.com/SergeiyVaryukhin/haralickt-1.0.0](https://github.com/SergeiyVaryukhin/haralickt-1.0.0)]

This is my first public release (v1.0.0), so I would really appreciate any feedback, bug reports, or ideas for new features. If you find it useful for your projects, a star on GitHub would mean a lot!