Fashion Style Image Dataset
Fashion Style Data
- Labeling Type: Fashion Style
- Data Format: Image
- Data Type: Synthetic Data
Main product
Data Quantity (Samples)
Total Price
$ 11,500
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About Dataset
1) Data Introduction
• This dataset provides images and information on various fashion items reflecting Korean Fashion (K-Fashion) styles. The data is classified into 10 styles: ""Feminine"", ""Sporty"", ""Avant-garde"", ""Oriental"", ""Western"", ""Genderless"", ""Country"", ""Classic"", ""Kitsch"", and ""Tomboy"".2) Data Utilization
(1) Characteristics of K-Fashion Style Data has characteristics that: • The dataset includes images of fashion items corresponding to each style, which are useful for understanding and analyzing the characteristics of these styles. • It provides information that helps in understanding the unique features and trends of each fashion style. (2) Application of K-Fashion Style Data can be used to: • Fashion analysis and trend prediction: Fashion designers and brands can utilize this data to analyze current trends and predict future fashion trends. • Development of recommendation systems: It can be used to develop personalized fashion recommendation systems on e-commerce platforms, offering tailored recommendations based on customers' style preferences. • Machine learning and computer vision research: It can be utilized to train and evaluate machine learning models in areas such as style classification and image recognition. • Cultural research: Analyzing the diverse styles of Korean fashion can be utilized for cultural research and educational materials. • Digital content creation: It can be used to create digital content for blogs, social media, online magazines, etc., to showcase and promote various styles."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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