Dataset Card for Kitti

The Kitti dataset. The Kitti object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects

Virtual KITTI is a photo-realistic synthetic video dataset designed to learn and evaluate computer vision models for several video understanding tasks: object detection and multi-object tracking, scene-level and instance-level semantic segmentation, optical flow, and depth estimation. Virtual KITTI contains 21,260 images generated from five different virtual worlds in urban settings under different imaging and weather conditions. These photo-realistic synthetic images are automatically, exactly, and fully annotated for 2D and 3D multi-object tracking and at the pixel level with category, instance, flow, and depth labels.

This dataset is based on The KITTI Vision Benchmark Suite/Object Detection Evaluation created by Andreas Geiger, Philip Lenz and Raquel Urtasun in the Conference on Computer Vision and Pattern Recognition (CVPR), 2012, "Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite". The data set contains two classes: - Vehicles – extracted images using the given labels with the coordinates, named classes ['Car', 'Truck', 'Van', 'Misc'] and minimum bounding box height: 40 Px and resized to 128 Px x 128 Px - Non-vehicles – randomly extracted images which don’t contain vehicles with some modifications to decrease the number of images with sky and flora of size 128 Px x 128 Px

KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation.

Source

Link: https://www.cvlibs.net/datasets/kitti/eval_depth.php?benchmark=depth_prediction

Eigen Split

Link: https://github.com/cleinc/bts

dpdl-benchmark/kitti dataset hosted on Hugging Face and contributed by the HF Datasets community

KITTI

## Overview

KITTI is a dataset for object detection tasks - it contains P annotations for 3,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Context

This Dataset contains KITTI Visual Odometry / SLAM Evaluation 2012 benchmark, created by Andreas Geiger, Philip Lenz and Raquel Urtasun in the Proceedings of 2012 CVPR ," Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite".

This is KITTI Visual Odometry dataset first 11 sequences that I have removed moving object like person and cars from images. I first trained the yoloV5 network on the Argoverse dateset to find objects that could move.With the help of this network, moving objects were found, then all the pixels inside the square of the detected object were set to (0,0,0).

Acknowledgements

Ruslan Baynazarov had downloaded this dataset from the link above and uploaded it on kaggle unmodified. Thank you Ruslan

This dataset consists of four ZIP files containing annotated images used for experiments in the research of formal specification and specification-based testing for image recognition in autonomous driving systems. The dataset has been derived and modified from the KITTI dataset.

• image1.zip: Contains 349 images. These images are part of the first subset used in the experiments.

• label1.zip: Contains the 2D bounding box annotations for vehicles corresponding to the images in image1.zip. There are 349 annotation files, and in total, 2,736 vehicles are annotated.

• image2.zip: Contains 1,300 images. These images are part of the second subset used in the experiments.

• label2.zip: Contains the 2D bounding box annotations for vehicles corresponding to the images in image2.zip. There are 1,300 annotation files, and in total, 5,644 vehicles are annotated.

The dataset was utilized in the research project focused on Bounding Box Specification Language (BBSL), a formal specification language designed for image recognition in autonomous driving systems. This research explores specification-based testing methodologies for object detection systems.

The BBSL project and related testing tools can be accessed on GitHub: https://github.com/IOKENTOI/BBSL-test.

The original KITTI dataset used for modification can be found at [KITTI dataset source link].
If you use this dataset, please cite the original KITTI dataset:
@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}
}

The KITTI val split is a subset of the KITTI dataset, used for validation and testing.

Chaitanya89/KITTI dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset is created by MonoTTA: Fully Test-Time Adaptation for Monocular 3D Object Detection, based on KITTI. You can check this link for more details: https://arxiv.org/abs/2405.19682v1 And access the code: https://github.com/Hongbin98/MonoTTA Please double-check the demands of KITTI when you try to download this dataset and obey their rules.

The Virtual KITTI dataset is a synthetic dataset, where the virtual scenes are cloned from the real world KITTI video sequences. Besides the 5 virtual image sequences cloned from KITTI sequence, it also generates the corresponding image sequences under various lighting conditions (like morning, sunset) and weather conditions (like fog, rain), totally 17,000 image frames.

KITTI Training

## Overview

KITTI Training is a dataset for object detection tasks - it contains Cars Trucks Pedestrians Vans Cyc annotations for 5,223 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

kitti dataset2011_09_262011_09_282011_09_292011_10_03

Yearly citation counts for the publication titled "Vision meets robotics: The KITTI dataset".

The KITTI Dataset is an authoritative multimodal dataset in the field of autonomous driving

Vuha123/KITTI dataset hosted on Hugging Face and contributed by the HF Datasets community

Mono KITTI is a dataset of high-quality monocular images with precise distance measurements, derived from the KITTI dataset. It is ideal for monocular distance estimation, autonomous driving, and advancing computer vision research.

Dataset Card for KITTI Stereo 2012

  Dataset Description

The KITTI Stereo 2012 dataset is a widely used benchmark dataset for evaluating stereo vision, optical flow, and scene flow algorithms in autonomous driving scenarios. It was introduced in the paper "Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite" by Geiger et al. Stereo matching refers to the process of estimating depth from two images captured from slightly different viewpoints—typically a… See the full description on the dataset page: https://huggingface.co/datasets/randall-lab/kitti-stereo2012.

Results on KITTI, using models trained on Cityscapes (from Cityscapes to KITTI) (%).