Retail Product Dataset

Retail Product Dataset


Retail Product Dataset


Retail Product Dataset

Use Case

Retail Product Dataset


Explore the Indonesian Retail Product Dataset, featuring high-resolution images and detailed annotations of popular products like Indomie, Chitato, and Aqua.


As Artificial Intelligence (AI) technology continues to evolve, it is creating increasingly accurate and efficient models. Despite these advancements, not all sectors have fully harnessed the power of AI, with retail and shopping being notable examples. A common experience in retail is the long, tedious queue at checkout, where customers have to wait for cashiers to manually scan each item. This process is time-consuming and inefficient. Some retailers, such as Amazon with their Dash Cart, have started using AI and object detection technology to expedite the checkout process, enhancing customer experience by significantly reducing wait times. However, the adoption of such technology remains limited, particularly in Indonesia, primarily due to the lack of accessible datasets.

To address this gap, our team has compiled a dataset aimed at assisting researchers and practitioners in developing object detection models, which can then be implemented by retailers globally, with a special focus on Indonesia.

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About the Dataset

The Indonesian Retail Product Dataset comprises images of six popular products frequently found in Indonesian retail stores. These products were chosen to represent a diverse range of retail items, facilitating the development of versatile object detection models. The selected products are Indomie (instant noodles), Chitato (potato chips), Aqua (bottled water), Pepsodent (toothpaste), tissue, and shampoo.

Dataset Information

  1. Products Included:
    • Indomie: A well-known brand of instant noodles, commonly purchased in Indonesian households.
    • Chitato: A popular brand of potato chips, favored for its unique flavors and wide availability.
    • Aqua: Indonesia’s leading bottled water brand, essential in daily consumption.
    • Pepsodent: A widely used toothpaste brand, reflecting the personal care segment.
    • Tissue: A staple product in both personal and household care categories.
    • Shampoo: Representing the hair care segment, this product is essential in daily grooming routines.
  2. Dataset Composition:
    • Images: High-resolution images of each product from multiple angles and lighting conditions to ensure model robustness.
    • Annotations: Detailed annotations marking the boundaries of each product within the images to aid in object detection tasks.
    • Metadata: Comprehensive metadata including product category, brand, and other relevant attributes to facilitate detailed analysis.
  3. Applications:
    • Checkout Automation: Development of AI models that can be integrated into smart checkout systems, reducing the need for manual scanning.
    • Inventory Management: Enhanced accuracy in tracking product inventory through automated recognition systems.
    • Retail Analytics: Improved data collection on product placement and consumer behavior through real-time detection and tracking.
  4. Benefits:
    • Efficiency: Significantly reduces the time customers spend in checkout lines, improving overall shopping experience.
    • Scalability: Provides a foundation for developing scalable AI solutions applicable to various retail environments.
    • Innovation: Encourages the adoption of cutting-edge technology in the retail sector, fostering a more innovative market landscape.

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