Horse Racing Photo Dataset

Image Resolutions:

Each image is captured and made available in three sizes:

• Small (200×96 pixels): Optimized for quick, low-resolution analysis.
• Medium (450×217 pixels): Balances detail and file size for moderate analysis.
• Large (478×230 pixels): High-resolution images for precise, in-depth research.

CSV Metadata:

Accompanying the images, a CSV file contains critical metadata about each race:

• Walk Type: Differentiates between Trot and Gallop, fundamental gait types in horse racing, essential for analyzing movement patterns.
• Speciality & Discipline: These sub-categories give further details on the race type, providing researchers with more context for analysis.
• Rope Direction: The position of the track’s barrier is noted as either on the left or right side of the horses, influencing lane dynamics and photo finish placement.
• Weather Conditions: Detailed weather codes allow insights into the environmental conditions affecting race visibility and performance:
  • P1 – P17: Codes range from sunny and cloudy to adverse weather like thunderstorms and snow, offering a broad spectrum for training models on different environmental factors.

Additional Environmental Factors:

• Luminosity: Whether the race occurred during day or night is an important factor, providing training data for models that operate under various lighting conditions, enhancing the dataset’s versatility.

Potential Applications:

This dataset is suitable for numerous machine learning applications:

• Photo Finish Analysis: Machine learning models can be trained to detect race winners based on these images.
• Environmental Impact Studies: The weather data enables research into how different weather conditions affect race outcomes.
• Gait Classification: Using the trot and gallop metadata, researchers can develop algorithms that classify different horse movements automatically.

Why This Dataset Stands Out:

• Versatility: With varied image sizes, weather conditions, and gait types, this dataset supports a broad range of research.
• Rich Metadata: The dataset provides thorough race information that offers a deeper context for understanding the nuances of horse racing.

This dataset provides an excellent resource for building models related to sports analytics, environmental conditions, and gait classification. The variety in race conditions and photo finish details ensures its suitability for complex machine learning projects.