From 75a05d2b6e21f6cb89404459b20c2703ac123a4f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?K=C3=A9vin=20Cortacero?= Date: Tue, 17 Dec 2024 15:33:05 +0100 Subject: [PATCH] Update README.md --- README.md | 22 ++++++++++++---------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index c0a4c2a..020bcef 100644 --- a/README.md +++ b/README.md @@ -1,27 +1,29 @@ -

Evolutionary design of explainable algorithms for biomedical image segmentation

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Kartezio Official Python Package

+[![Discord Channel](https://dcbadge.limes.pink/api/server/uwFwHyRxub)](https://discord.gg/KnJ4XWdQMK) ---- +

Kartezio: Evolutionary design of explainable algorithms for biomedical image segmentation

-**Kartezio** is a modular Cartesian Genetic Programming (CGP) framework that enables the automated design of fully interpretable image-processing pipelines, without the need for GPUs or extensive training datasets. Built on top of [OpenCV](https://opencv.org/), Kartezio empowers researchers, engineers, and practitioners to discover novel computer vision (CV) solutions using only a handful of annotated samples and a single CPU core. -Originally developed for biomedical image segmentation, Kartezio has been successfully showcased in [Nature Communications](https://www.nature.com/articles/s41467-023-42664-x). Although it shines in medical and life science applications, Kartezio’s underlying principles are domain-agnostic. Whether you’re working with industrial quality control, satellite imagery, embedded vision, or robotics, Kartezio helps you craft custom CV pipelines that are **transparent, fast, frugal and efficient**. +**Kartezio** is a modular Cartesian Genetic Programming (CGP) framework that enables the automated design of fully interpretable image-processing pipelines, without the need for GPUs or extensive training datasets. +Built on top of [OpenCV](https://opencv.org/), Kartezio empowers researchers, engineers, and practitioners to discover novel computer vision (CV) solutions using only a handful of annotated samples and a single CPU core. + +Originally developed for biomedical image segmentation, Kartezio has been successfully showcased in [Nature Communications](https://www.nature.com/articles/s41467-023-42664-x). Although it shines in medical and life science applications, Kartezio’s underlying principles are domain-agnostic. +Whether you’re working with industrial quality control, satellite imagery, embedded vision, or robotics, Kartezio helps you craft custom CV pipelines that are **transparent, fast, frugal and efficient**. ## Key Features -:nut_and_bolt: **Modular and Customizable** +:nut_and_bolt: **Modular and Customizable** Kartezio is built from interchangeable building blocks, called **Components**, that you can mix, match, or replace. Adapt the pipeline to your project’s unique requirements. -:pencil2: **Few-Shot Learning** +:pencil2: **Few-Shot Learning** Forget the need for massive, annotated datasets. Kartezio can evolve solutions from just a few annotated examples, saving both time and computational resources. -:white_check_mark: **Transparent and Certifiable** +:white_check_mark: **Transparent and Certifiable** Every pipeline produced is fully transparent. Inspect the exact operations used, understand their sequence, and trust the decisions made by your model. -:earth_africa: **Frugal and Local** +:earth_africa: **Frugal and Local** Run everything on a single CPU, without GPUs or massive compute clusters. This makes Kartezio ideal for edge devices, embedded systems, or scenarios with limited computational resources. -:microscope: **Broad Applicability** +:microscope: **Broad Applicability** While proven in biomedical image segmentation, Kartezio’s methods readily extend to other fields—like industrial machine vision, space imaging, drone footage analysis, or any custom image-based problem. ## Getting Started