kitti

• Description:

Kitti contains a suite of vision tasks built using an autonomous driving platform. The full benchmark contains many tasks such as stereo, optical flow, visual odometry, etc. This dataset contains the object detection dataset, including the monocular images and bounding boxes. The dataset contains 7481 training images annotated with 3D bounding boxes. A full description of the annotations can be found in the readme of the object development kit readme on the Kitti homepage.

• Homepage: http://www.cvlibs.net/datasets/kitti/
• Source code: tfds.object_detection.kitti.Kitti
• Versions:
  • 3.1.0 (default): No release notes.
  • 2.0.0: New split API (https://tensorflow.org/datasets/splits)
• Download size: 11.71 GiB
• Dataset size: Unknown size
• Auto-cached (documentation): No
• Splits:

Split	Examples
'test'	711
'train'	6,347
'validation'	423

• Features:

FeaturesDict({
    'image': Image(shape=(None, None, 3), dtype=tf.uint8),
    'image/file_name': Text(shape=(), dtype=tf.string),
    'objects': Sequence({
        'alpha': Tensor(shape=(), dtype=tf.float32),
        'bbox': BBoxFeature(shape=(4,), dtype=tf.float32),
        'dimensions': Tensor(shape=(3,), dtype=tf.float32),
        'location': Tensor(shape=(3,), dtype=tf.float32),
        'occluded': ClassLabel(shape=(), dtype=tf.int64, num_classes=4),
        'rotation_y': Tensor(shape=(), dtype=tf.float32),
        'truncated': Tensor(shape=(), dtype=tf.float32),
        'type': ClassLabel(shape=(), dtype=tf.int64, num_classes=8),
    }),
})

• Supervised keys (See as_supervised doc): None
• Citation:

@inproceedings{Geiger2012CVPR,
  author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
  title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2012}
}