Tesla Car Models Dataset
A comprehensive collection of images of Tesla's flagship electric vehicles, including Model S, Model 3, Model X, and Model Y, for automated vehicle recognition and brand recognition research.
- Labeling Type: model
- Data Format: Image
- Data Type: Synthetic Data
Main Product
Data Quantity (Samples)
Total Price
$ 4,800
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About Dataset
1) Data Introduction
• The Tesla Car Models Dataset is a classification dataset built from images of Tesla’s flagship electric vehicles: Model S, Model 3, Model X, and Model Y. Each model has distinctive visual and functional characteristics, making this dataset suitable for EV model classification, brand recognition, and computer vision-based vehicle identification model training.2) Data Utilization
(1) Characteristics of the Tesla Car Models Dataset: • The dataset consists of images categorized by Tesla’s four major models, reflecting visually distinguishable features such as exterior design, proportions, and color variations. • It includes images taken in both indoor and outdoor environments, making it well-suited for domain generalization evaluation and practical model training. (2) Applications of the Tesla Car Models Dataset: • Vehicle model classification: Can be used to train deep learning models that classify Tesla vehicles into Model S, 3, X, or Y based on visual input. • Electric vehicle brand recognition research: Useful for developing automated systems that identify Tesla vehicles by learning their unique design features.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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