Shells or Pebbles: An Image Classification Dataset
A binary classification dataset designed for a specific task of distinguishing between shells and pebbles.
- Labeling Type: Object type
- Data Format: Image
- Data Type: Synthetic Data
Main Product
Data Quantity (Samples)
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$ 6,000
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About Dataset
1) Data Introduction
• The Shells or Pebbles: An Image Classification Dataset is a computer vision dataset designed for a binary classification task that distinguishes between shells and pebbles. The dataset consists of two classes (Shells and Pebbles), and each image is used to determine whether the object is a shell or a pebble.2) Data Utilization
(1) Characteristics of the Shells or Pebbles: An Image Classification Dataset: • The dataset is designed to help models learn and distinguish subtle visual differences between shells and pebbles, which often share similar shapes and textures. • It contains images captured under varied backgrounds and conditions, making it suitable for training models with strong generalization capabilities. (2) Applications of the Shells or Pebbles: An Image Classification Dataset: • Development of binary classification models (Shell vs. Pebble): The dataset can be used to train deep learning models that classify images as either shell or pebble. • Educational use for visual recognition tasks: This dataset is also suitable for training in shape-, texture-, and edge-based feature extraction and pattern recognition, making it a valuable resource for teaching and experimentation in computer vision.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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