Blue Crab Dataset
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Detailed information on blue crabs, including images and JSON format data of adult blue crabs and blue crab seeds, aimed at building AI training data for AI analysis and prediction.
- Labeling type: Growth Stages
- Data Format: Image
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About Dataset
1) Data Introduction
• Blue Crab Dataset provides images and information on blue crabs at various growth stages, captured in hatchery environments. It includes images and JSON format data of adult blue crabs and blue crab seeds, aimed at building AI training data for AI analysis and prediction.2) Data Utilization
(1) Blue Crab Data has characteristics that: • The dataset includes images of blue crabs at different growth stages, making it useful for understanding and analyzing the characteristics of these crustaceans. • It provides labeled data that helps in understanding the growth stages and environmental factors affecting the survival and development of blue crabs. (2) Blue Crab Data can be used to: • Development of growth stage classification models: AI models can be trained to classify the growth stages of blue crabs, aiding in the management and optimization of hatchery environments. • Survival and health monitoring: The data can be used to develop models that predict the health and survival rates of blue crabs, assisting in the preservation and enhancement of blue crab seed production technologies.Meta Data
Very good
Outstanding
Data Samples 1
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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