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Product Overview

Experience the best in each step of your deep learning project

Model Types

Deep Learning Model Types

6 Types of Deep Learning Models

Neurocle Software provides 6 types(Classification, Segmentation, Object Detection, Anomaly Detection, OCR, and Rotation) of deep learning vision models. 

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Data Management

Efficient and secure data management

Upload an manage images in various formats. This data is safely stored on your local private network.

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Data Management

Data Management

Tag & Flag for efficient collaboration

Categorize and manage data conveniently with Tag & Flag. Group images by multicolored tags, and easily spot models and label sets by flags.

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Easy data preprocessing

Preprocess your images in many forms without additional software. Crop, rotate, and transform your images however you like.



Convenient labeling :
Auto-labeling & Magic wand

Minimize labeling resources by using Auto-labeling that automatically finds the desired area and the Magic wand, a selection tool for specific areas. Other labeling tools are also supported to increase user convenience.

Model Training

Model Training

One-click training
for various models

Anyone can create and optimize various deep learning models at the click of a button, even without knowledge of deep learning.

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Model Design & Review

Model Design & Review

Design and Review Complex Models without Programming:
Flowchart & Inference Center

Predict inference results and review your project before deploying the models trained in Neuro-T/Neuro-X to Neuro-R for runtime inference.
Connect multiple models and display the predicted inference results in the GUI. Modify the model design until desired inference time is reached, minimizing trial and error during actual deployment.

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Clear results through automatic reporting

Simplify how you view and manage of training results. An automatically generated report displays the training results displayed in organized charts and tables.



Optimized application to the platform of choice

From low-spec embedded boards to CPUs and GPUs, optimize the trained model for various computing devices and OSs to deploy it on-site.

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