Ginseng Age Identification and Size Classification Dataset
Detailed images of ginseng classified by age and size, making it useful for recognizing and analyzing the characteristics of different ginseng grades.
- Labeling Type: Size and Old
- Data Format: Image
- Data Type: Synthetic Data
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Data Quantity (Samples)
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$ 14,400
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About Dataset
1) Data Introduction
• Ginseng Age Identification and Size Classification Dataset provides images and information on ginseng classified by age (4-year, 5-year, and 6-year-old) and size (large, medium, and small). The dataset includes 9 categories combining age and size, with images collected for AI training to identify the age and grade of ginseng.2) Data Utilization
(1) Ginseng Age Identification and Size Classification Data has characteristics that: • The dataset includes detailed images of ginseng classified by age and size, making it useful for recognizing and analyzing the characteristics of different ginseng grades. • It provides information helpful for improving the efficiency and accuracy of quality inspections and preventing fraudulent distribution. (2) Ginseng Age Identification and Size Classification Data can be used to: • Development of AI-based ginseng grading models: The AI model can be trained to recognize and classify ginseng by age and size, aiding in the automation of quality inspection processes. • Enhancement of smart farming practices: By using this data, farmers can improve the efficiency of ginseng cultivation and reduce management costs, addressing issues such as aging populations in farming communities.Meta Data
Good
Outstanding
Data Samples 0
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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