Stanford Dogs Dataset
A high-resolution image dataset consisting of 17 representative dog breeds, including Maltese, Shih Tzu, Afghan Hound, Irish Wolfhound, Saluki, Scottish Deerhound, Sealyham Terrier, Airedale, Tibetan Terrier, Bernese Mountain Dog, Entlebucher, Basenji, Pug, Leonberger, Great Pyrenees, Samoyed, and Pomeranian.
- Labeling Type: breed
- Data Format: Image
- Data Type: Synthetic Data
Related Tags
Main Product
Data Quantity (Samples)
Total Price
$ 7,500
(VAT Included)Looking for custom-made dataset or researcher-accessible data? Please contact us for inquiries.
About Dataset
1) Data Introduction
• The Stanford Dogs dataset is a high-resolution image dataset consisting of 17 representative dog breeds, including Maltese, Shih Tzu, Afghan Hound, Irish Wolfhound, Saluki, Scottish Deerhound, Sealyham Terrier, Airedale, Tibetan Terrier, Bernese Mountain Dog, Entlebucher, Basenji, Pug, Leonberger, Great Pyrenees, Samoyed, and Pomeranian.2) Data Utilization
(1) Characteristics of the Stanford Dogs Dataset: • The dataset is well-suited for capturing subtle visual differences between dog breeds with similar appearances and can be effectively used for fine-grained breed classification and image recognition experiments. (2) Applications of the Stanford Dogs Dataset: • Dog Classification Model Development: It can be used to develop artificial intelligence models that automatically classify dog species by learning the visual features of various dog species. • Detailed Image Recognition Study: It can be used for research in the field of fine-grained visual categorization that distinguishes minute differences between dog species with similar appearance.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.
Premium Report Information
Premium dataset sample
