Clothes Dataset
Clothing images collected from Carousell, an online marketplace, and is utilized in various computer vision tasks such as clothing classification, fashion recommendation systems, virtual try-on applications, and fashion trend analysis.
- Labeling Type: clothing
- Data Format: Image
- Data Type: Synthetic Data
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$ 5,000
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About Dataset
1) Data Introduction
• The Clothes Dataset is a classification dataset composed of clothing images collected from Carousell, an online marketplace. It consists of 15 clothing categories (e.g., T-shirts, shirts, jackets, dresses, etc.), and can be utilized in various computer vision tasks such as clothing classification, fashion recommendation systems, virtual try-on applications, and fashion trend analysis.2) Data Utilization
(1) Characteristics of the Clothes Dataset: • The dataset contains clothing images with a wide range of colors, textures, and styles, making it highly suitable for realistic fashion item recognition tasks. (2) Applications of the Clothes Dataset: • Clothing image classification model development: Can be used to train deep learning models that automatically classify images into 15 clothing categories. • Fashion recommendation and virtual try-on systems: Useful for building AI models that recommend appropriate clothing based on user preferences or body silhouettes.Meta Data
Normal
Outstanding
Data Samples 5
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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