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  6. Automatically create high-precision training data with the "Automatic Annotation Service."

Automatically create high-precision training data with the "Automatic Annotation Service."

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last updated:Jul 17, 2026

東芝情報システム
東芝情報システム
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We automatically create a large amount of high-precision training data in a short period using an annotation automation tool!

Do you have challenges with "creating training data" in the fields of embedded systems, automotive, and robotics? ● Creating training data is time-consuming and costly, hindering development ● Manual annotation leads to variability in quality ● A large amount of training data is needed, but cannot be handled manually ● You want to accelerate PoC and validation, but data preparation is a bottleneck            ▼ Leave it to our automatic annotation service! ―― Features ――――――――――――――――――――――― ◆ Our unique AI technology automatically generates high-precision training data from a small number of training examples ◆ Supports semantic segmentation and bounding boxes ◆ Creates a large volume of training data in a short time, significantly shortening AI development periods ――――――――――――――――――――――――――――― The automatic annotation service is an engineering service that provides a large volume of high-precision training data for image annotation in a short period of time, using tools based on Toshiba Information Systems' unique AI technology, with a small dataset of learning data and target image data provided according to your annotation specifications.

    Software (middle, driver, security, etc.)Business Intelligence and Data AnalysisOther services
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Automatically create high-precision training data with the "Automatic Annotation Service."

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  • Related Link - https://www.tjsys.co.jp/embedded/auto-annotation/i…
  • Special site - Product/service details - https://pr.mono.ipros.com/tjsystem/product/

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■What is Annotation in AI Development Annotation refers to the process of adding meaning (tags/labels) to the data that you want to train the AI on, thereby creating correct data. This serves as the foundational data for the AI model to learn from and is an essential step to ensure the model learns correctly through machine learning. The quality of the annotation significantly affects the performance of the AI model, making it an important task. ■Benefits of Automating Annotation <Cost Reduction> Creating a large amount of training data requires a lot of manpower. By automating this process, labor costs associated with manual work can be reduced. The more data you handle, the greater the cost-saving effect. <Efficiency in AI Development> By automating the creation of training data, you can prepare a large amount of high-quality learning data with consistent quality in a short period, ultimately contributing to the efficiency of AI development. ■Three Features of Automated Annotation Tools and Services - Automatically create high-precision training data - Support automation of semantic segmentation and bounding boxes - Significantly reduce the time for automatic generation, achieving world-class accuracy for training data

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■Introduction of Achievements - Provision of Automatic Annotation Tools to Annotation Service Companies In November 2023, our company announced a collaboration with TT PM Co., Ltd. in the annotation service for creating training data used in AI (Artificial Intelligence) machine learning. This service provides an automatic annotation tool that can create large volumes of high-quality training data in a short time, which determines the quality of AI. ■Usage Scenarios for Automatic Annotation Services - Automotive (e.g., autonomous driving): Supports road surface recognition for vehicles with segmentation - Security: Supports person detection for surveillance cameras with bounding boxes - Medical: Supports cell recognition in pathology images with segmentation

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東芝情報システム

東芝情報システム

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We will provide optimal solutions that contribute to our customers' businesses through our "extensive experience and achievements accumulated over many years" and "high technical capabilities" in the fields of embedded and LSI design.

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