Sheep Breed Classification Dataset
A comprehensive and detailed understanding of sheep breeds, allowing for the development of deep learning models for sheep classification and the integration of smart farm management systems.
- Labeling Type: breed
- Data Format: Image
- Data Type: Synthetic Data
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$ 6,000
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About Dataset
1) Data Introduction
• The Sheep Breed Classification Dataset is a multi-class image classification dataset for computer vision, constructed using real-world images of sheep captured on an actual farm in Australia. It consists of images categorized into four sheep breeds: Marino, Poll Dorset, Suffolk, and White Suffolk.2) Data Utilization
(1) Characteristics of the Sheep Breed Classification Dataset: • The dataset consists of frame-by-frame images extracted from video footage of sheep being drafted on a farm, thereby reflecting visual conditions similar to real livestock farming environments. (2) Applications of the Sheep Breed Classification Dataset: • Development of Sheep Breed Classification Models: By learning facial features and external traits of different sheep breeds, this dataset can be used to train deep learning models that automatically classify sheep breeds. Such models can contribute to automated livestock identification and data-driven livestock management in the agricultural sector. • Smart Farm Management Systems: The automatic breed recognition feature can be integrated into smart farming systems to support livestock history tracking, health monitoring, and reproductive planning, enabling more intelligent and efficient farm operations.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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