Fast Food Classification Dataset
Designed for image classification of various fast food items, consisting of a total of 10 classes.
- Labeling Type: food type
- Data Format: Image
- Data Type: Synthetic Data
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Data Quantity (Samples)
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$ 6,600
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About Dataset
1) Data Introduction
• The Fast Food Classification Dataset is designed for image classification of various fast food items, consisting of a total of 10 classes: Burger, Donut, Hot Dog, Pizza, Sandwich, Baked Potato, Crispy Chicken, Fries, Taco, and Taquito.2) Data Utilization
(1) Characteristics of the Fast Food Classification Dataset: • The dataset includes images captured under various lighting conditions, backgrounds, and angles, making it suitable for evaluating the generalization performance of classification models in real-world scenarios. (2) Applications of the Fast Food Classification Dataset: • Training fast food image classification models: Can be used to develop deep learning–based image classifiers that accurately distinguish between various fast food items. • Building automated food recognition systems: Applicable to real-time food identification and classification in self-order kiosks, smart POS systems, and food recognition apps.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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