Science Score: 36.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
✓Academic publication links
Links to: zenodo.org -
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (6.7%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: KLIVIS
- License: apache-2.0
- Language: Python
- Default Branch: main
- Size: 12.5 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 1
- Releases: 0
Metadata Files
README.md
IVD-SEG
https://github.com/KLIVIS/IVD-SEG/blob/main/README.md
abstract
We introduce IVD-SEG, a large-scale standardized industrial defect image dataset similar to MNIST. The dataset includes 12 subsets of images that are all unified to a size of 256256 with semantic segmentation annotations for users to use when unfamiliar with industrial product defects. The IVD-SEG dataset covers defect images of 43 different products in industrial scenes, including fabrics, roads, metals, and magnetic tiles, among others. There are a total of 5686 images, covering both binary and multi-class segmentation tasks. In the absence of easy access to industrial defect images, we propose the first large-scale industrial defect dataset containing multiple product categories, which helps advance research on industrial defect detection and segmentation technology. Additionally, as image segmentation is a universal task, our dataset supports research and education in multiple fields, such as computer vision and machine learning. We benchmark several baseline methods on IVD-SEG, including representative CNN and VIT networks.
Data
Aavilable at Zenodo
Owner
- Name: IVIS
- Login: KLIVIS
- Kind: user
- Location: Shenzhen, China
- Company: SZU
- Repositories: 1
- Profile: https://github.com/KLIVIS