BDD100K Period Classification

BDD100K Period Classification


BDD100K Period Classification


BDD100K Period Classification

Use Case

Computer Vision


The BDD100K dataset, unveiled by Berkeley AI Research (BAIR) in May 2018, is a comprehensive and diverse driving video dataset.


BDD100K Period Classification

The BDD100K dataset, disclosed by Berkeley AI Research (BAIR) in May 2018, is a comprehensive and diverse driving video dataset. It features 100,000 high-definition videos (720p at 30 fps) and a comparable set of 100,000 images excerpt at the 10-second mark of each video (resolution of 1280×720). These images are comment with various details, including object adjacent boxes, drivable areas, and lane markers. The dataset is structured into 70,000 training, 10,000 validation, and 20,000 testing samples, making it ideal for training and evaluating AI models in object detection and road feature recognition. This dataset spans diverse geographical, environmental, and weather conditions, offering a robust resource for AI development in autonomous driving technologies.

  1. Part of the large-scale Berkeley DeepDrive (BDD100K) dataset.
  2. Aims to enhance autonomous driving research.
  3. Includes diverse driving scenarios captured at different times of the day.
  4. Classifies images into four periods: dawn/dusk, daytime, nighttime, and other.
  5. Used to train and evaluate models for time-specific detection and classification tasks.
  6. Improves robustness of autonomous vehicle systems.
  7. Features diverse lighting and weather conditions.
  8. Ideal for developing models that operate reliably under different circumstances.
  9. Enhances safety and performance of autonomous driving technologies.
  10. Supports advanced research in autonomous vehicle development.


The BDD100K Period Classification dataset is crucial for advancing autonomous driving research. By providing diverse scenarios and time-specific classifications, it helps develop robust models that improve the safety and performance of autonomous vehicles under various lighting and weather conditions.

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