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Mobiusi/Bottled-Goods-Image-Classification-Dataset

Bottled Goods Image Classification Dataset The retail e-commerce industry currently faces challenges such as low efficiency and poor accuracy in product classification, affecting inventory management and user experience. Existing image classification solutions often rely on limited sample sizes and simple feature extraction methods, resulting in insufficient model generalization capability. This dataset aims to address the problem of insufficient samples in image classification… See the full description on the dataset page: https://huggingface.co/datasets/Mobiusi/Bottled-Goods-Image-Classification-Dataset.

sourceHugging Facecc-by-nc-sa-4.0updated 7mo agoView on Hugging Face

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Dataset Card

Bottled Goods Image Classification Dataset

The retail e-commerce industry currently faces challenges such as low efficiency and poor accuracy in product classification, affecting inventory management and user experience. Existing image classification solutions often rely on limited sample sizes and simple feature extraction methods, resulting in insufficient model generalization capability. This dataset aims to address the problem of insufficient samples in image classification by providing a large number of high-quality images of bottled goods, thereby enhancing classification accuracy and efficiency. Data collection is done using professional photographic equipment under standard lighting conditions to ensure image quality. Quality control measures include multiple rounds of annotation and expert review to ensure the accuracy of each image's label. The data storage format is JPG, organized by category, facilitating subsequent processing and use. The core advantage of this dataset is a labeling accuracy of over 95%, strong sample consistency, and high completeness. By introducing new labeling methods and data augmentation techniques, the model's accuracy in classification tasks is improved by 10%. Moreover, this dataset provides a reliable solution for product identification in retail e-commerce, significantly improving inventory management efficiency.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
category_label	string	The category of the repackaging bottle product as described by the image.
brand_name	string	The brand name that the product belongs to.
product_color	string	The primary color of the product.
material_type	string	The primary material used in the repackaging bottle.
cap_type	string	The type of cap used on the bottle, such as twist cap, flip cap, etc.
label_text	string	Text information visible on the label of the bottle.
bottle_shape	string	The overall shape of the repackaging bottle, such as round, square, etc.
imagequalityscore	float	A quantitative evaluation score of the image quality.
background_complexity	string	The complexity of the image background described as simple, moderate, complex, etc.
image_orientation	string	The orientation of the image, such as horizontal, vertical, etc.

Compliance Statement

<table> <tr> <td>Authorization Type</td> <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td> </tr> <tr> <td>Commercial Use</td> <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td> </tr> <tr> <td>Privacy and Anonymization</td> <td>No PII, no real company names, simulated scenarios follow industry standards</td> </tr> <tr> <td>Compliance System</td> <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td> </tr> </table>

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com