The KITTI 3D Object Detection dataset, pivotal for autonomous driving research, contains 7481 training images and 7581 testing images. Each training image is accompanied by a label file detailing object coordinates in the image plane. The dataset categorizes objects into nine classes: Car, Truck, Van, Tram, Pedestrian, Cyclist, Person_sitting, Misc, and DontCare. Analysis shows an imbalanced distribution among these classes; for instance, the Car class significantly outnumbers others, while Person_sitting has notably fewer examples. This imbalance can lead to biases in statistical learning models, potentially underperforming on less represented classes. The dataset also highlights the challenge of detecting small-sized objects, with certain classes like Pedestrian and Cyclist having smaller average bounding box sizes, necessitating specialized detection techniques.
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