Flowers Recognition Dataset
A diverse range of real-world images with varying resolutions, aspect ratios, and backgrounds, making it an ideal resource for practicing tasks such as preprocessing, normalization, and data augmentation.
- Labeling Type: Flower type
- Data Format: Image
- Data Type: Synthetic Data
Main Product
Data Quantity (Samples)
Total Price
$ 7,000
(VAT Included)Looking for custom-made dataset or researcher-accessible data? Please contact us for inquiries.
About Dataset
1) Data Introduction
• The Flowers Recognition Dataset is a multi-class image classification dataset consisting of flower images. It includes five categories: daisy, dandelion, rose, sunflower, and tulip.2) Data Utilization
(1) Characteristics of the Flowers Recognition Dataset: • The dataset contains real-world images with varying resolutions and aspect ratios, making it suitable for practicing tasks such as preprocessing, normalization, and data augmentation. • The diversity in background, lighting, and composition makes the dataset appropriate for evaluating model performance in realistic scenarios. (2) Applications of the Flowers Recognition Dataset: • Development of plant recognition applications: Can be used as training data for mobile apps or photo-based plant identification services. • Training multi-class image classification models: Useful for building deep learning models that automatically classify flower types and for experimenting with transfer learning.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.
Premium Report Information
Premium dataset sample
