Model all your ideas.

Ikomia offers a complete visual AI toolbox for developers to seamlessly model, train and deploy image processing solutions.

Visual AI toolbox

How it works ?

01

Import your data

Ikomia Studio makes it easy to open your images, videos, video streams and annotated datasets.

02

Model

Model your idea quickly with state-of-the-art algorithms and create your first PoC with Ikomia Studio and Ikomia Marketplace.

03

Deploy

Deploy your Computer Vision solution on a cloud server or embedded system with Ikomia API.

Import your data

Import your data easily into Ikomia Studio using drag and drop.

Model

Prototype your model quickly and create your first POC with Ikomia Studio.

Deploy

Deploy your Computer Vision solution on cloud servers or on IoT devices with Ikomia API.

Visual AI toolbox

Ikomia Studio

Ikomia Marketplace

Ikomia API

Appliquez votre algorithmes sur vos données en un clic.

Gardez un oeil sur votre workflow.

Évaluez les résultats.

Gardez un oeil sur votre workflow.

Inférence TransUNet pour la segmentation sémantique.

Papier : TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A-L. Yuille, Y Zhou. Preprint 2021

Code : github.com/Beckschen/TransUNet

Inférence TransUNet pour la segmentation sémantique.

Modèle d’inférence DeepLabv3+ de Detectron2 pour la segmentation sémantique.

Papier : Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam. ECCV 2018

Code : github.com/facebookresearch/detectron2

Ikomia API permet d’exécuter vos algorithmes sur n’importe quel système embarqué en quelques lignes de codes.

#JetsonNano #OAK-D #RaspberryPi #GoogleCoral

Appliquez votre algorithmes sur vos données en un clic.

Gardez un oeil sur votre workflow.

Évaluez les résultats.

Gardez un oeil sur votre workflow.

Inférence TransUNet pour la segmentation sémantique.

Papier : TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A-L. Yuille, Y Zhou. Preprint 2021

Code : github.com/Beckschen/TransUNet

Inférence TransUNet pour la segmentation sémantique.

Modèle d’inférence DeepLabv3+ de Detectron2 pour la segmentation sémantique.

Papier : Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam. ECCV 2018

Code : github.com/facebookresearch/detectron2

Ikomia API permet d’exécuter vos algorithmes sur n’importe quel système embarqué en quelques lignes de codes.

#JetsonNano #OAK-D #RaspberryPi #GoogleCoral

Apply an algorithm to your data in one click.

Keep an eye on your workflow.

Assess the results.

Set up your algorithms easily.

TransUNet
TransUNet

TransUNet inference for semantic segmentation.

Paper : TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A-L. Yuille, Y Zhou. Preprint 2021

Code : github.com/Beckschen/TransUNet

Detectron2 DeepLabV3Plus

DeepLabv3+ inference model of Detectron2 for semantic segmentation.

Paper : Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam. ECCV 2018

Code : github.com/facebookresearch/detectron2

visual AI API

The Ikomia API is a Python library that makes it easy to model your visual AI application. Whether it’s a workflow from Ikomia Studio or a workflow from scratch, any developer can easily run state-of-the-art algorithms on any type of machine (desktop, compute servers, cloud computing) with a few lines of code.

Ikomia Studio

visual AI Zoo

Ikomia Marketplace

TransUNet
TransUNet inference for semantic segmentation.

Paper : TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. J. Chen, Y. Lu, Q. Yu, X. Luo, E. Adeli, Y. Wang, L. Lu, A-L. Yuille, Y Zhou. Preprint 2021

Code : github.com/Beckschen/TransUNet

Detectron2 DeepLabV3Plus

DeepLabv3+inference model of Detectron2 for semantic segmentation.

Paper : Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam. ECCV 2018

Code : github.com/facebookresearch/detectron2

Ikomia API

visual AI API

The Ikomia API is a Python library that makes it easy to model your visual AI application. Whether it’s a workflow from Ikomia Studio or a workflow from scratch, any developer can easily run state-of-the-art algorithms on any type of machine (desktop, compute servers, cloud computing) with a few lines of code.

Our tools assets

Open Source

We strongly believe in knowledge sharing, which is why we develop Open Source tools.

You can prototype and model your visual AI applications freely with our tools.

#Ikomia Studio #Ikomia Marketplace

Time Saving

Thanks to our tools, you save time in your prototyping, modeling and deployment of your visual AI applications.

You don’t need to be an expert to start a visual AI project.

#Ikomia Studio #Ikomia Marketplace

Marketplace

We integrate the best state-of-the-art algorithms to make them usable in 1 click.

Algorithms are taken from available repositories in all major fields of Computer Vision (classification, detection, segmentation, pose…).

#Ikomia Marketplace

No code tools

For beginner developers, you can use all of our algorithms without writing a single line of code.

Test OpenCV in 1 click or start using Deep Learning algorithms effortlessly.

#Ikomia Studio #Ikomia Marketplace

Low code solution

For more expert developers, integrate your own algorithms into Ikomia Studio to test, compare and improve them.

Benefit from our Python API that allows you to manage computing servers (local or cloud) in a few lines of code.

#Ikomia Studio

Deploy on Cloud and IoT

Computing infrastructure management can be complicated to set up, which is why we offer a turnkey solution that allows you to deploy your visual AI applications on Ikomia servers or on your own compute servers.

#Ikomia API

Start your project

Ikomia Apps resources

Python API documentation

Get started on how you can create your first Python Ikomia App. Let’s dig into our simple API and how you can enjoy the strength of Ikomia.

Ikomia GitHub

Ikomia Studio (AGPLv3), Ikomia API (LGPL) and all our algorithms are open source. Please visit our GitHub repo for more information.