Women's Fashion by Era Dataset
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Women's Fashion Data by Era
- Labeling type: Women's Fashion by Era
- Data Format: Image
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About Dataset
1) Data Introduction
• This dataset provides images and information necessary for analyzing and recommending women's fashion styles by decade. It includes files reflecting women's fashion trends from the 1950s to 2019 in 10-year increments.2) Data Utilization
(1) Women's Fashion Data by Era has characteristics that: • The dataset includes images of fashion items corresponding to women's fashion styles of each era, making it useful for understanding and analyzing the fashion characteristics of different periods. (2) Women's Fashion Data by Era can be used to: • Fashion Analysis and Trend Prediction: Fashion designers and brands can utilize this data to analyze current and predict future fashion trends of each era. • Development of Recommendation Systems: It can be used to develop personalized women's fashion recommendation systems on e-commerce platforms, providing tailored recommendations based on customer preferences and styles. • Machine Learning and Computer Vision Research: It can be utilized for training and evaluating machine learning models in fields such as fashion style classification and image recognition. • Cultural Studies: Analyzing women's fashion trends of each era can contribute to cultural research and educational materials. • Digital Content Creation: Utilize this data for creating digital content on blogs, social media, online magazines, etc., to introduce and promote women's fashion trends.Meta Data
Good
Outstanding
Data Samples 4
Utility
| Downstream Classification (▲) | KID (▼) | One Class Classification (▼) | |
|---|---|---|---|
| Total | 0 | 0 | 0 |
| Suitability | OK | OK | OK |
The higher the value, the better (▲)
Model Performance
Downstream classification accuracy is an indicator used to evaluate the usefulness of synthetic data. It measures whether synthetic data performs similarly to real data. The method involves training the same model separately on real data and synthetic data, and then comparing the accuracies of the two models. Interpretation: A high accuracy rate means that the model trained on synthetic data performs similarly to the one trained on real data, indicating that the synthetic data is of high quality and well represents the real data.
The closer to zero or the lower the value, the better (▼)
Quality
KID (Kernel Inception Distance) is a metric used to evaluate the similarity between generated images and real images. It compares the differences between the two sample distributions using Kernel Mean Embedding, without assuming a normal distribution. Interpretation: A lower KID score suggests that generated images are more similar to real images, with a score close to 0 being ideal. Specifically, a score below 0.01 indicates very high similarity.
Privacy
| LPIPS (▲) | SSIM (▼) | |
|---|---|---|
| Total | 0 | 0 |
| Suitability | OK | OK |
The higher the value, the better (▲)
Perceptual Similarity
Learned Perceptual Image Patch Similarity (LPIPS) is a metric used to measure the visual similarity between two images by utilizing neural networks to extract key features and calculate the distance between them. High LPIPS value: Indicates high similarity between images, raising the risk of information leakage. Low LPIPS value: Suggests that synthetic images are perceptually different from real images, indicating a lower risk of sensitive information leakage.
The closer to zero or the lower the value, the better (▼)
Structural Similarity
The Structural Similarity Index Measure (SSIM) is a metric used to assess the similarity between two images. It is primarily used to compare the quality of a restored or compressed image with the original image. SSIM measures visual similarity by considering brightness, contrast, and structure.
High SSIM value (0.9 or above): Indicates that the synthetic image is very similar to the real image, which may increase the risk of information leakage.
Low SSIM value (0.6 or below): Indicates low similarity and reduced risk of leakage.
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