ccl-core
commented
7 months ago Thank you @Yashsharma009 for opening the issue! Just to understand, are you planning on adding the dataset implementation yourself, or is this open for the community to tackle?
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Content:
100,000 diverse driving scene images: Captures a wide range of real-world driving scenarios, including various weather conditions, locations, and traffic situations. Rich annotations: Includes detailed object bounding boxes, lane markings, and drivable areas for comprehensive scene understanding. Multiple tasks: Supports object detection, semantic segmentation, lane line detection, and more, enabling multifaceted research and development. Common use cases:
Autonomous driving research: Used extensively for training and evaluating deep learning models for object detection, scene understanding, and control systems in autonomous vehicles. Computer vision research: Serves as a benchmark for developing and testing new algorithms for tasks such as object segmentation, image classification, and image retrieval in various driving scenarios. Robotics research: Facilitates research in navigation, path planning, and decision-making for autonomous robots operating in complex environments. Multitask learning: The diverse tasks within BDD10K make it ideal for exploring techniques that can learn multiple tasks simultaneously, potentially improving model efficiency and performance.>
Folks who would also like to see this dataset in
tensorflow/datasets, please thumbs-up so the developers can know which requests to prioritize.And if you'd like to contribute the dataset (thank you!), see our guide to adding a dataset.