cityscape dataset structure
Can a large-scale dataset be used for urban scene understanding?
Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes.
Wait, What’s Fiftyone?
FiftyOneis an open source machine learning toolset that enables data science teams to improve the performance of their computer vision models by helping them curate high quality datasets, evaluate models, find mistakes, visualize embeddings, and get to production faster. The FiftyOne Dataset Zoo comprises more than 30 datasets, with new datasets be
About The Cityscapes Dataset
The Cityscapes Dataset is a 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. At the time of its release it was an order of magnitude larger than similar
What Is Visual Scene Understanding?
Scene understanding is the process of perceiving, analyzing and elaborating an interpretation of a 3D dynamic scene observed through a network of sensors. This usually involves matching signal information coming from the sensors observing the scene, with machine learning models humans are using to understand the scene. As a result, scene understand
Labeling Policy
Labeled foreground objects must never have holes. For example if there is some background visible ‘through’ some foreground object, it is considered to be part of the foreground. This also applies to regions that are highly mixed with two or more classes: they are labeled with the foreground class. Some examples would include: 1. tree leaves in fro
Class Definitions
* Single instance annotations are available. However, if the boundary between such instances cannot be clearly seen, the whole crowd/group is labeled together and annotated as group, e.g. car group. + This label is not included in any evaluation and treated as void (or in the case of license plate as the vehicle mounted on). voxel51.com
Dataset Quick Facts
Research Paper: The Cityscapes Dataset for Semantic Urban Scene UnderstandingAuthors:M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. SchieleDownload Dataset: Register and downloadLicense: Free, but registration is required voxel51.com
Step 1: Download The Dataset
In order to load the Cityscape Dataset into FiftyOne, you must download the source data manually with your source_dirorganized in the following manner: Note that the gtFine_trainvaltest, gtCoarse, and gtBbox_cityPersons_trainvalare optional directories. voxel51.com
Step 2: Install Fiftyone
If you don’t already have FiftyOne installed on your laptop, it takes just a few minutes For example on macOS: 1. Verify your versionof Python 2. Create and activate a virtual environment 3. Install IPython(optional) 4. Upgrade your Setuptools 5. Install FiftyOne Learn more about how to get up and running with FiftyOnein the Docs. voxel51.com
Step 3: Import The Dataset
Now that you have the dataset downloaded and FiftyOne installed, let’s import the dataset into FiftyOne and launch the FiftyOne App. This should take just a few minutes and a few more lines of code. The last line in the code snippet will launch the FiftyOne App in your default browser. You should see the following initial view of the cityscapes-val
Filtering by ID
FiftyOne makes it very easy to filter the samples to find the ones that meet your specific criteria. For example we can filter by a specific id: voxel51.com
Cityscapes semantic segmentation with augmentation tutorial Pytorch (part1)
Cityscapes Dataset
Cityscapes Dataset
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The Cityscapes Dataset
more challenging datasets have been created to push forward the text for methods exploiting optical flow tracking |
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The Cityscapes Dataset for Semantic Urban Scene Understanding
7 avr. 2016 We compare Cityscapes to other datasets in terms of (i) ... fewer flat ground structures fewer humans |
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The Cityscapes Dataset for Semantic Urban Scene Understanding
As a result KITTI exhibits significantly fewer flat ground structures |
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The Cityscapes Dataset for Semantic Urban Scene Understanding
We compare Cityscapes to other datasets in terms of (i) annotation volume and density (ii) the fewer flat ground structures |
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The Cityscapes Dataset for Semantic Urban Scene Understanding
As a result KITTI exhibits significantly fewer flat ground structures |
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Improving Training on Noisy Stuctured Labels
8 mars 2020 structures in the annotations and in the noisy la- ... For the Cityscapes dataset we used a U-Net architecture for the standard network f. |
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Auto-DeepLab: Hierarchical Neural Architecture Search for
6 avr. 2019 ing Cityscapes PASCAL VOC 2012 |
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Semantic Segmentation of Remote-Sensing Imagery Using
12 oct. 2020 (ISPRS) Potsdam and Cityscape datasets are used as the RS and natural-image ... containing an object which can deliver high-level structure ... |
| Unsupervised Monocular Depth and Ego-motion Learning with |
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Universal Semantic Segmentation for Fisheye Urban Driving Images
24 août 2020 (Convolutional Neural Networks) structure design. Blott et ... when we use Cityscapes dataset [3] to train a semantic. |
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Paper - Cityscapes Dataset
The Cityscapes Dataset more challenging datasets have been created to push forward the text for methods exploiting optical flow, tracking, or structure- |
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The Cityscapes Dataset
[7,18,22]) and more challenging datasets have been created 1 structure (8) building houses, skyscrapers wall not part of a building fence traffic sign |
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The Cityscapes Dataset for Semantic Urban Scene Understanding
fence Structures with holes that separate two (or more) outdoor areas, sometimes temporary guard rail2 Metal structure located on the side of the road to prevent |
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Learning Multi-Class Segmentations From Single-Class Datasets
the Cityscapes dataset We further curate segmentation of various structures is a fundamen- structure of interest, image quality, and the clinician's expe- |
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IDD: A Dataset for Exploring Problems of Autonomous Navigation in
is similar for our dataset and Cityscapes [5], the number of object classes and Segmentation and recognition using structure from mo- tion point clouds |
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Semantic Segmentation Datasets for Resource Constrained Training
introduced Cityscapes [5] and India Driving Dataset [16](IDD) which provide to come up with innovative architectures or algorithms for structure learning or |
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Benchmarking Robustness in Object Detection - Machine Learning
reduction to 33 rPC on the Cityscapes dataset, which contains many small objects With some high RMSE) while leaving local structure unchanged |
