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Universal Cloth Parsing and Segmentation Labels
Fashion & Apparel Trends
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About
Providing a vital foundation for computer vision tasks, these records facilitate the training of segmentation models such as Unet and Resnet for cloth parsing. Cloth parsing involves the detailed identification and categorisation of clothing items within an image, which is an essential precursor for advanced applications like virtual try-on technology. By offering a structured set of labels, this resource assists developers in building high-quality parsers that can accurately distinguish between different garment segments.
Columns
- Output: The numeric Class ID representing a specific segment or category of clothing used during the model training process.
- Description: The textual label or name corresponding to the Class ID, identifying the specific type of garment or material.
Distribution
The information is delivered in a CSV format titled
labels_Cloth_Parsing.csv with a file size of 71 B. It consists of six unique records structured across two columns. The data exhibits total integrity with 100% validity and no mismatched or missing values, providing a reliable mapping for classification tasks. This resource has achieved a usability score of 10.00 and is updated on a monthly basis.Usage
This collection is specifically designed for training and validating deep learning models used in image segmentation and cloth parsing. It is ideal for developers creating virtual fitting rooms, fashion recommendation engines, or automated garment categorisation tools. Researchers can also utilise these labels to benchmark the performance of various neural network architectures in the field of computer vision.
Coverage
The scope is focused on the fundamental labels required for cloth parsing and segmentation. It provides six distinct class definitions that serve as the ground truth for image classification. While the demographic or geographic origin of the original images is not specified, the labels represent a standard categorisation system for garment identification in a digital environment.
License
CC0: Public Domain
Who Can Use It
Machine learning engineers can leverage these labels to refine the accuracy of segmentation algorithms. Fashion technology developers may find this data essential for building the backend logic of virtual try-on systems. Additionally, computer science students can use this high-integrity set to learn the basics of label mapping and class identification in image processing.
Dataset Name Suggestions
- Universal Cloth Parsing and Segmentation Labels
- Virtual Try-On Training: Cloth Parsing Classes
- Neural Network Cloth Segmentation Label Archive
- Garment Classification and Parsing ID Registry
- Cloth Parsing Dataset for Unet and Resnet Training
Attributes
Original Data Source:Universal Cloth Parsing and Segmentation Labels
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