#Japan #Tokyo #AI Training #Qlean Dataset #Visual Bank

Visual Bank Introduces Qlean Dataset

Visual Bank Inc., based in Shibuya, Tokyo and led by CEO Masayuki Nagai, is making strides in the AI training data sector through its subsidiary, Amana Images. The company has recently expanded its offerings by launching the Qlean Dataset, a unique data solution designed for various research and commercial AI development projects.

This latest addition to their lineup is the "Japanese Face Image Dataset with and without Accessories," which plays a crucial role in enhancing the existing AI development resources provided by Qlean Dataset. Notably, this dataset includes images of 200 individuals, meticulously captured under a range of conditions, showcasing their faces adorned with various accessories like masks and glasses, as well as without them.

Overview of the Japanese Face Image Dataset

Dataset Composition and Details

• - Included Data: 20,000 images of 200 models with accessories and different shooting conditions.
• - Number of Participants: 200 individuals in total.
• - Data Format: The images are stored in JPEG format.
• - File Count: The dataset contains a total of 22,800 images.
• - Data Size: Approximately 3.99GB in total.
• - Resolution: Most images are 1,080×1,920, with some at 4,608×3,456.
• - Shooting Conditions: Taken from distances suitable for capturing the full face and upper body, ensuring quality close-up shots.

With advancements in AI becoming increasingly vital for various industries, this dataset empowers developers by allowing immediate and flexible use of data tailored to their distinct needs. Their special offering known as 'Data Recipe' facilitates the creation of original, commercially usable data lines. This unique provision simplifies the data assembly process by allowing users to easily mix and match data components based on requirements such as accuracy and timelines.

Use Cases for the Dataset

The applications for the dataset are extensive within the realm of AI development:
1. Enhancing Face Recognition AI: By incorporating diverse images of faces with and without accessories, developers can significantly improve recognition accuracy, making it applicable in real-world scenarios where various adornments come into play.
2. Robust Face Detection Model Development: Featuring a diverse demographic spanning different ages and genders, this dataset aids in creating resilient face detection algorithms that are less impacted by age or emotional expression variations.
3. Facial Recognition for Security Systems: The variety in distances and lighting conditions captured within this dataset allows for accurate facial recognition model training and evaluation, vital for security management applications in offices and public spaces.
4. Eyewear Recognition and Attribute Estimation: The dataset provides avenues for multi-task learning where it can assist in discerning eyewear presence and provide insights into fashion attributes.
5. AI Fairness and Bias Evaluation: The balanced representation of age and gender in the dataset enables effective assessment and calibration of attribute biases that can arise in facial recognition algorithms.
6. Optimizing Edge Devices: The varied image resolutions facilitate model compression techniques, crucial for maintaining high-performance facial recognition on devices with limited computational resources.

Key Features of Qlean Dataset

• - Fully Compliant for Research and Commercial Use: Users can be assured of compliance with international privacy policies, having obtained consent from all subjects involved, allowing for secure research and commercial application.
• - Data Recipe Utilization: The unique 'Data Recipe' approach facilitates swift data procurement while maximizing return on investment.
• - Custom Data Solutions: For users requiring highly specialized datasets, Qlean Dataset can develop tailored solutions addressing unique needs.

Potential collaborators can explore the dataset through the Qlean Dataset website, where additional details and services are available:
Qlean Dataset

For inquiries and partnerships, visit the contact page: Inquiries

Visual Bank Background

Visual Bank Inc. is dedicated to building a next-generation data infrastructure aimed at unleashing the potential of various data forms. Its mission is supported by a subsidiary, Amana Images, dedicated to AI dataset development, ensuring robust support in the evolving landscape of AI technologies.