Mold Template Defect Recognition Dataset
#Image Classification #Object Detection #Industrial Inspection #Quality Control #Defect Detection
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-04-13
AI Analysis & Value Prop
In the industrial sector, mold quality inspection is crucial for ensuring product integrity but faces challenges such as inconsistent defect detection and high rates of false negatives. Existing solutions often rely on manual inspection, which is time-consuming and prone to human error, leading to inefficiencies. This dataset aims to address these challenges by providing a comprehensive collection of labeled images that enhance machine learning models for accurate defect recognition in mold templates. Data was collected using high-resolution cameras in controlled industrial environments, ensuring clarity and consistency. Quality control measures included multiple rounds of labeling, consistency checks among annotators, and expert reviews to maintain high accuracy. The images are stored in JPG format, organized in directories by defect type, facilitating easy access and processing.
Dataset Insights
Sample Examples
8422f40d**.jpg|1080*1414|150.78 KB
ddfdfae2**.jpg|1074*1888|238.71 KB
a4e76f92**.jpg|1080*1403|222.62 KB
f4d4b8ee**.jpg|1920*1038|187.54 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| defect_type | string | The types of defects appearing on the mold, such as cracks and pores. |
| defect_location | string | A detailed description of the defect's specific location on the mold. |
| severity_level | integer | The severity level of the defect as defined by the standards. |
| bounding_box_coordinates | string | Coordinates of the bounding box that marks the defect in the target detection task. |
| surface_condition | string | The overall condition of the mold surface, including the presence of oxidation or wear. |
| light_condition | string | The lighting conditions when capturing the image, such as natural light or artificial light. |
| image_quality | string | The quality assessment of the image, such as clarity or blurriness. |
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 Mold Template Defect Recognition Dataset?
- The Mold Template Defect Recognition Dataset is an image dataset containing a large number of mold defect images, designed to improve the efficiency and accuracy of mold quality inspection.
- Which industrial sectors is this dataset suitable for?
- This dataset is suitable for industrial sectors that require mold quality inspection, such as manufacturing and mold production.
- How does the Mold Template Defect Recognition Dataset help improve quality inspection?
- By providing a large number of high-quality mold defect images, the dataset can be used to train object detection algorithms, improving the accuracy and efficiency of defect detection.
- How are the images in the dataset annotated?
- The images in the dataset are annotated with information on defect locations and categories for the training of object detection models.
- Why choose images as the data modality?
- Images can visually present the mold defects and are easily processed by visual detection algorithms, making them an ideal data modality for training object detection models.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Mold Template Defect Recognition Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/a866579a56bc44abdf0e8fed3a6ccb09?dataset_scene_id=2},
urldate={2025-08-28},
keywords={mold defect detection,industrial quality control,image dataset for defect recognition},
version={1.0}
}Using this in research? Please cite us.
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