Tyre Quality Classification Dataset
A binary image classification computer vision dataset designed to distinguish the quality condition of car tyres, consisting of low-noise visual information well-suited for training quality assessment models.
- Labeling Type: quality condition
- Data Format: Image
- Data Type: Synthetic Data
Main product
Data Quantity (Samples)
Total Price
$ 6,800
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About Dataset
1) Data Introduction
• The Tyre Quality Classification Dataset is a binary image classification computer vision dataset designed to distinguish the quality condition of car tyres. Each image is labeled as either "Good" (normal) or "Defective" (damaged).2) Data Utilization
(1) Characteristics of the Tyre Quality Classification Dataset: • The dataset consists of images captured from a front-facing perspective of a single tyre, providing low-noise visual information well-suited for training quality assessment models. • Each tyre is clearly labeled as either good or defective, allowing for the effective training of precision models that can recognize visual indicators such as wear, cracks, or deformation. (2) Applications of the Tyre Quality Classification Dataset: • Development of AI Models for Tyre Defect Detection: This dataset can be used to train deep learning-based inspection models that automatically classify tyres based on visual features (wear, damage, defects, etc.). • Automation of Quality Control in the Automotive Industry: The dataset supports the development of vision-based quality inspection systems capable of automatically diagnosing tyre conditions in real time—whether in manufacturing lines or service centers—contributing to accident prevention and improved maintenance 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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