Images of medicinal herbs (from Donguibogam) Dataset
Images of medicinal herbs recorded in the Dongui Bogam, which can be used to study and research the unique features of each herb, develop herbal recognition systems, create digital content, and promote herbal products.
- Labeling Type: Herbal Classification
- Data Format: Image
- Data Type: Synthetic Data
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$ 11,500
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About Dataset
1) Data Introduction
• This dataset includes images of medicinal herbs mentioned in the Dongui Bogam. The data consists of images sourced from AIHub and is classified into 10 categories: ""Trumpet Creeper"", ""Baby Trumpet Creeper"", ""Globe Artichoke"", ""Round-leaf Trumpet Creeper"", ""Bellflower Root"", ""Garden Angelica"", ""Common Aster"", ""Artemisia Argyi"", ""Dandelion", and ""White Dandelion".2) Data Utilization
(1) Images of medicinal herbs (from Donguibogam) data has characteristics that: • The dataset includes images of each herb, which are useful for understanding and analyzing the characteristics of the herbs. • It provides images that help in understanding the unique features of each herb as recorded in the Dongui Bogam. (2)Images of medicinal herbs (from Donguibogam) data can be used to: • Herbal research and education: Researchers and students in traditional medicine can use this data to study and research the characteristics of medicinal herbs recorded in the Dongui Bogam. • Development of herbal recognition systems: It can be used to develop systems that automatically recognize and classify herbs using machine learning and computer vision technologies. • Cultural research: Analysis of various medicinal herbs recorded in the Dongui Bogam can be utilized for cultural research and educational materials. • Digital content creation: It can be used to create digital content for blogs, social media, online magazines, etc., to introduce and promote medicinal herbs recorded in the Dongui Bogam. • Healthcare and wellness: It can be utilized to research the efficacy and usage of herbs for developing healthcare and wellness-related products.Meta Data
Good
Outstanding
Data Samples 4
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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