Chips Product Occlusion Image Dataset
#Object Detection #Image Classification #Product Recognition #Visual Search #Inventory Management
- 15000 records
- 3.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-11-04
AI Analysis & Value Prop
The retail e-commerce industry faces significant challenges in accurately identifying products in images, especially when occlusions occur. Existing solutions often struggle with occluded items, leading to inefficiencies in inventory management and product recognition. This dataset aims to address the specific technical issue of occlusion detection in product images, fulfilling the need for more robust visual recognition systems in retail applications. The dataset is structured to include images of popular chip brands such as Lay's, Pringles, and Doritos, with various levels of occlusion. Data collection involved capturing images in diverse retail settings, utilizing high-resolution cameras to ensure clarity. Quality control measures included multiple rounds of annotations, consistency checks among annotators, and expert reviews to ensure data accuracy. The images are organized in JPG format for efficient storage and retrieval.
Dataset Insights
Sample Examples
383e7558**.png|1500*1664|2.66 MB
a9cdd76a**.png|1500*1953|2.20 MB
e1cad090**.png|1500*1959|3.74 MB
4e30682b**.png|1500*1842|3.22 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| occlusion_level | string | The degree to which chip products are occluded in the image, such as no occlusion, partial occlusion, or severe occlusion. |
| chip_brand | string | The brand information of the chip products in the image. |
| chip_flavor | string | The flavor information of the chip products in the image. |
| package_condition | string | The condition of the chip packaging in the image, such as intact or damaged. |
| angle_of_view | string | The viewpoint at which the image was taken, such as front view or side view. |
| lighting_condition | string | The lighting conditions during image capture, such as well-lit, dim, or overexposed. |
| background_clutter | string | The level of clutter in the image background, such as simple, moderate, or complex. |
| reflection_presence | boolean | Whether there are reflections present in the image. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What is the Chips Product Occlusion Detection Image Dataset?
- The Chips Product Occlusion Detection Image Dataset is a collection of chip product images with various occlusion scenarios, aimed at improving the accuracy of product recognition.
- Which industry field does this dataset belong to?
- This dataset belongs to the retail industry, focusing on product recognition.
- What is the purpose of chip product occlusion detection?
- The purpose of chip product occlusion detection is to improve the accuracy of recognizing products in various occlusion conditions.
- What technology type is used in this dataset?
- This dataset uses object detection technology to recognize and locate chip products in images.
- How does this dataset help improve product recognition?
- This dataset helps improve product recognition by providing images with various occlusion scenarios, enhancing machine learning models' capabilities in real-world settings.
Can't find the data you need?
Post a request and let data providers reach out to you.
Get this Dataset
Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Chips Product Occlusion Image Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/4a32937870be31086e60dc5e6203db6e?dataset_scene_id=9},
urldate={2025-08-28},
keywords={chips dataset,occluded product images,retail e-commerce dataset,image recognition,object detection dataset},
version={1.0}
}Using this in research? Please cite us.
placeholder
placeholder
placeholder
placeholder
placeholder
placeholder
placeholder