cityscape dataset labels

What is the cityscapes dataset?
The Cityscapes Dataset focuses on semantic understanding of urban street scenes. In the following, we give an overview on the design choices that were made to target the dataset’s focus. See Class Definitions for a list of all classes and have a look at the applied labeling policy. Preceding and trailing video frames.
Does cityscapes support pixel level annotations?
For segmentation tasks (default split, accessible via 'cityscapes/semantic_segmentation'), Cityscapes provides dense pixel level annotations for 5000 images at 1024 * 2048 resolution pre-split into training (2975), validation (500) and test (1525) sets.
How do I Root a cityscapes dataset?
root the root folder of the Cityscapes dataset. Many of our scripts check if an environment variable CITYSCAPES_DATASET pointing to this folder exists and use this as the default choice. type the type/modality of data, e.g. gtFine for fine ground truth, or leftImg8bit for left 8-bit images.

PDF	The Cityscapes Dataset The labels are encoded in different colors. Note that instances of traffic participants are annotated individually. semantic understanding of urban scenarios is

PDF	The Cityscapes Dataset for Semantic Urban Scene Understanding 7 avr. 2016 To maximize synergies between both datasets a com- mon label definition that allows for cross-dataset evaluation.

PDF	The Cityscapes Dataset for Semantic Urban Scene Understanding Table 8 provides precise definitions of our annotated classes. These definitions were used to guide our labeling process as well as quality control.

PDF	The Cityscapes Dataset Our annotations enable out-of-the-box evaluations for scene labeling at the image and instance level as well as object detection. We discuss the benchmark

PDF	The Cityscapes Dataset for Semantic Urban Scene Understanding semantic labeling by also considering instance-level seman- tic labeling in both our annotations and evaluation metrics. To facilitate research on 3D scene

PDF	Weakly-Supervised Semantic Segmentation in Cityscape via

PDF	The Cityscapes Dataset for Semantic Urban Scene Understanding We go beyond pixel-level semantic labeling by also considering instance-level seman- tic labeling in both our annotations and evaluation metrics. To facilitate

PDF	The Cityscapes Dataset for Semantic Urban Scene Understanding We go beyond pixel-level semantic labeling by also considering instance-level seman- tic labeling in both our annotations and evaluation metrics. To facilitate

PDF	Cityscapes-Panoptic-Parts and PASCAL-Panoptic-Parts datasets for 16 avr. 2020 Both datasets have annotations compatible with panoptic segmentation and additionally they have part-level labels for selected semantic classes.

PDF	Improving Training on Noisy Stuctured Labels 8 mars 2020 We use versions of two public datasets the GMB dataset for NLP tagging and the Cityscapes dataset for image segmentation. See Section 4 for ...

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PDF	Paper - Cityscapes Dataset dataset The labels are encoded in different colors Note that instances of traffic participants are annotated individually semantic understanding of urban

PDF	The Cityscapes Dataset for Semantic Urban Scene Understanding We go beyond pixel-level semantic labeling by also considering instance-level seman- tic labeling in both our annotations and evaluation metrics To facilitate

PDF	The Cityscapes Dataset for Semantic Urban Scene Understanding Table 8 provides precise definitions of our annotated classes These definitions were used to guide our labeling process, as well as quality control In addition

PDF	Addressing Domain Shift for Semantic - CVF Open Access time for obtaining pixel-wise labels for a single image from *First two authors the CITYSCAPES dataset is about 1 hr , highlighting the level of difficulty ([4],

PDF	Benchmark Suite – Cityscapes Dataset 12 sept 2019 · https://www cityscapes-dataset com/benchmarks/ The first Cityscapes task involves predicting a per-pixel semantic labeling of the image

PDF	IDD: A Dataset for Exploring Problems of Autonomous Navigation in The label set is expanded in comparison to popular benchmarks such as Cityscapes, to account for new classes It also re- flects label distributions of road scenes

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Cityscapes Panoptic Parts Dataset — Cityscapes Panoptic Parts Dataset

Examples – Cityscapes Dataset — Examples – Cityscapes Dataset

Dataset Overview – Cityscapes Dataset — Dataset Overview – Cityscapes Dataset

Prepare Cityscapes dataset — gluoncv 0110 documentation — Prepare Cityscapes dataset — gluoncv 0110 documentation

Examples – Cityscapes Dataset — Examples – Cityscapes Dataset

PDF) Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection — PDF) Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection

IJGI — IJGI

Examples – Cityscapes Dataset — Examples – Cityscapes Dataset

Examples – Cityscapes Dataset — Examples – Cityscapes Dataset

Semantic Foggy Scene Understanding with Synthetic Data — Semantic Foggy Scene Understanding with Synthetic Data

Examples – Cityscapes Dataset — Examples – Cityscapes Dataset

IJGI — IJGI

Examples – Cityscapes Dataset — Examples – Cityscapes Dataset

UAVid: A semantic segmentation dataset for UAV imagery - ScienceDirect — UAVid: A semantic segmentation dataset for UAV imagery - ScienceDirect

Cityscapes Dataset – Semantic Understanding of Urban Street Scenes — Cityscapes Dataset – Semantic Understanding of Urban Street Scenes

GTA5 Dataset — GTA5 Dataset

Examples – Cityscapes Dataset — Examples – Cityscapes Dataset

Treepedia 20: Applying Deep Learning for Large-scale — Treepedia 20: Applying Deep Learning for Large-scale

PDF) Data Selection for training Semantic Segmentation CNNs with — PDF) Data Selection for training Semantic Segmentation CNNs with

IJGI — IJGI

Examples – Cityscapes Dataset — Examples – Cityscapes Dataset

Detection and Retrieval of Out-of-Distribution Objects in Semantic — Detection and Retrieval of Out-of-Distribution Objects in Semantic

Improving Semantic Segmentation via Self-Training – arXiv Vanity — Improving Semantic Segmentation via Self-Training – arXiv Vanity

Semantic segmentation with OpenCV and deep learning - PyImageSearch — Semantic segmentation with OpenCV and deep learning - PyImageSearch

Improving Training on Noisy Stuctured Labels — Improving Training on Noisy Stuctured Labels

PDF) Image Segmentation: Domain Adaptation Through Data — PDF) Image Segmentation: Domain Adaptation Through Data

Examples – Cityscapes Dataset — Examples – Cityscapes Dataset

Semantic Segmentation on Indian Driving Dataset — Semantic Segmentation on Indian Driving Dataset

Improving Semantic Segmentation via Self-Training – arXiv Vanity — Improving Semantic Segmentation via Self-Training – arXiv Vanity

IJGI — IJGI

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