12 Garbage Classification Dataset
A comprehensive and fine-grained image classification dataset consisting of 12 categories of household waste, including paper, cardboard, biological waste, metal, plastic, green glass, brown glass, white glass, clothes, shoes, batteries, and general trash.
- Labeling Type: garbage type
- Data Format: Image
- Data Type: Synthetic Data
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Data Quantity (Samples)
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$ 6,300
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About Dataset
1) Data Introduction
• The Garbage Classification Dataset is an image classification dataset consisting of 12 categories of household waste: paper, cardboard, biological waste, metal, plastic, green glass, brown glass, white glass, clothes, shoes, batteries, and general trash.2) Data Utilization
(1) Characteristics of the Garbage Classification Dataset: • Composed of 12 fine-grained waste categories, the dataset is well-suited for training high-precision classification models that reflect real-world waste sorting conditions. (2) Applications of the Garbage Classification Dataset: • Development of automated household waste classification models: This dataset can be used to train deep learning-based image classifiers capable of sorting various types of waste, and can be applied to the development of smart separation systems that improve recycling efficiency.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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