https://cityscapes-dataset.com

Cityscapes Dataset – Semantic Understanding of Urban Street ScenesCityscapes Dataset – Semantic Understanding of Urban Street Scenes Skip to content Semantic Understanding of Urban Street Scenes Semantic Understanding of Urban Street ScenesNews Overview Features Labeling Policy Class Definitions Examples Fine Annotations Coarse Annotations Videos Benchmarks Pixel-Level Semantic Labeling Task Results Instance-Level Semantic Labeling Task Results Panoptic Semantic Labeling Task Results 3D Vehicle Detection Task Results Meta Information Download Submit Citation Contact Team Labs Privacy The Cityscapes Dataset Semantic, instance-wise, dense pixel annotations of 30 classes Dataset Overview The Cityscapes Dataset 5 000 images with high quality annotations · 20 000 images with coarse annotations · 50 different cities Dataset Overview The Cityscapes Dataset Rich metadata: preceding and trailing video frames · stereo · GPS · vehicle odometry Dataset Overview The Cityscapes Dataset Benchmark suite and evaluation server for pixel-level, instance-level, and panoptic semantic labeling Benchmark Suite ‹› Dataset Overview Get an overview of the Cityscapes dataset, its main features, the label policy, and the definitions of contained semantic classes.Read more » Examples Have a look at some examples providing further insights into the type and quality of annotations, as well as the metadata that comes with the Cityscapes dataset.Read more » Benchmark Suite Find out about the challenges in our benchmark suite, their corresponding metrics and the performance results of evaluated methods.Read more » The Cityscapes Dataset We present a new large-scale dataset that contains a diverse set of stereo video sequences recorded in street scenes from 50 different cities, with high quality pixel-level annotations of 5 000 frames in addition to a larger set of 20 000 weakly annotated frames. The dataset is thus an order of magnitude larger than similar previous attempts. Details on annotated classes and examples of our annotations are available at this webpage. The Cityscapes Dataset is intended for assessing the performance of vision algorithms for major tasks of semantic urban scene understanding: pixel-level, instance-level, and panoptic semantic labeling;supporting research that aims to exploit large volumes of (weakly) annotated data, e.g. for training deep neural networks. Latest News Cityscapes 3D Benchmark OnlineOctober 17, 2020Cityscapes 3D is an extension of the original Cityscapes with 3D bounding box annotations for all types of vehicles as well as a benchmark for the 3D detection task. For more details please refer to our paper, presented at the CVPR 2020 Workshop on Scalability in Autonomous Driving. Today, we extended our benchmark and evaluation server to include the 3D vehicle detection task. In order to train and evaluate your method, checkout our toolbox on Github, which can be installed using pip, i.e.python -m pip install cityscapesscripts[gui]. In order to visualize the 3D Boxes, run csViewer and select the CS3D… Read more: Cityscapes 3D Benchmark Online License This Cityscapes Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree to our license terms. Post navigation Dataset Overview → News Cityscapes 3D Benchmark Online October 17, 2020 Cityscapes 3D Dataset Released August 30, 2020 Coming Soon: Cityscapes 3D June 16, 2020 Robust Vision Challenge 2020 June 4, 2020 Panoptic Segmentation May 12, 2019 Search Website Search for: Contact Cityscapes Team Imprint / Impressum Data Protection / Datenschutzhinweis © 2024 Cityscapes Datasetenusen-US1740560292https://cityscapes-dataset.com

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