Mannequin Fashion Accessory Wear Dataset
Detailed images and annotations of various fashion accessories worn by mannequins, making it useful for AI training in augmented reality (AR) and virtual reality (VR) applications.
- Labeling Type: Fashion Accessories
- Data Format: Image
- Data Type: Synthetic Data
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Data Quantity (Samples)
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$ 15,100
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About Dataset
1) Data Introduction
• Mannequin Fashion Accessory Wear Dataset provides detailed images and annotations of various fashion accessories worn by mannequins. The dataset includes information on accessories such as earrings, hats, necklaces, sunglasses, and eyeglass frames, and is intended to be used for AI training in augmented reality (AR) and virtual reality (VR) applications.2) Data Utilization
(1) Mannequin Fashion Accessory Wear Data has characteristics that: • The dataset includes images of mannequins wearing different fashion accessories, making it useful for developing and training models for accessory recognition and virtual fitting. • It provides detailed segmentation and annotation information that is helpful for accurately detecting and classifying various fashion accessories. (2) Mannequin Fashion Accessory Wear Data can be used to: • Development of AR/VR virtual fitting services: The AI model can be trained to recognize and virtually fit accessories on users, enhancing online shopping experiences. • Fashion editing and virtual human creation: By using this data, developers can create virtual fashion editors and virtual humans for advertising and marketing, allowing for diverse styling options.Meta Data
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Outstanding
Data Samples 1
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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