Wood Household Waste Images Dataset
Images and information on industrial waste, especially Waste Wood Household, which can be used to understand the different types, compositions, and characteristics of waste wood household items, develop AI models for recognizing and classifying them, enhance waste management efficiency, promote material recovery and reuse, and aid in environmental impact studies.
- Labeling Type: Waste Wood Household
- Data Format: Image
- Data Type: Synthetic Data
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Data Quantity (Samples)
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$ 29,700
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About Dataset
1) Data Introduction
• This dataset provides images and information on industrial waste, especially Waste Wood Household.2) Data Utilization
(1) Waste Wood Household Data has characteristics that: • The dataset Contains images of various types of waste wood household items, useful for identifying and analyzing the characteristics of each type. • Provides information to understand the different types, compositions, and characteristics of waste wood household items. (2) Waste Wood Household Data can be used to: • Develop Recognition and Classification Models: AI models can be trained to recognize and classify different types of waste wood household items. For example, in recycling facilities, when waste wood items are scanned, the model can identify their type for proper processing. • Recycling and Waste Management: Useful for developing systems to sort and manage waste wood household items, enhancing efficiency in recycling and waste management operations. • Material Recovery and Reuse: Helps in identifying wood that can be recovered and reused from different types of waste household items, promoting sustainable practices. •Environmental Impact Studies: Provides data for analyzing the environmental impact of various types of waste wood household items, aiding in the development of eco-friendly waste management strategies.Meta Data
Excellent
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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