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--- |
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pretty_name: Cityscapes VPS |
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tags: |
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- image |
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- datasets |
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- webdataset |
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- pandas |
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- unipercept |
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license: afl-3.0 |
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task_categories: |
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- depth-estimation |
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- image-segmentation |
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- video-classification |
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- object-detection |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Cityscapes VPS |
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This dataset is derived from the videos in the *validation* split of the Cityscapes[^1] dataset. |
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It aggregates the images and metadata from Cityscapes[^1], Cityscapes-VPS[^2] and Cityscapes-DVPS[^3] into a single structured format. |
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This comprehensive derivative was created out of the need for a batteries-included variant of the dataset for academic purposes. |
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Specifically, joining samples from the individual datasets in their original structure (each is organized differently) involves a significant amount of boilerplate code. |
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This dataset is relevant to computer vision research areas such as: |
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- Segmentation |
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- Depth estimation |
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- Autonomous driving |
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- Video understanding |
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## Overview |
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The following variables are included. |
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1. **Images.** The input data captured by the left camera from Cityscapes[^1], in 8-bit format. Every sequence has 30 frames. |
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2. **Segmentation labels.** Derived from Cityscapes[^1] and Cityscapes-DVPS[^3], these labels provide detailed semantic segmentation and instance segmentation information for 6 frames of every sequence. |
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3. **Depth maps.** Improved depth information from Cityscapes-DVPS[^3], offering enhanced quality over the disparity package from Cityscapes[^1], provided for the same samples as the segmentation labels above. |
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4. **Camera calibrations.** Includes the intrinsic and extrinsic parameters provided by Cityscapes[^1] for each sequence. |
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5. **Vehicle odometry.** Odometry data for each frame, a subset of those provided in Cityscapes[^1]. |
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Files are grouped by split, sequence and frame. |
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This leads to the following structure: |
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```text |
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data |
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train |
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000000 |
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000000.image.png |
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000000.panoptic.png |
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000000.depth.tiff |
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000000.vehicle.json |
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000000.timestamp.txt |
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000001.image.png |
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000001.panoptic.png |
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000001.depth.tiff |
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000001.vehicle.json |
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000001.timestamp.txt |
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000000.camera.json |
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000001 |
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... |
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000001.camera.json |
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... |
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val |
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000000 |
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... |
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000000.camera.json |
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... |
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test |
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000000 |
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... |
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000000.camera.json |
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``` |
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The `data` directory in this repository only contains the segmentation and depth map annotations. |
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The remaining data should be downloaded from official sources using the provided preparation script. |
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## Preparation |
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1. Clone this dataset repository. |
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```bash |
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git clone https://huggingface.co/datasets/khwstolle/csvps && cd csvps |
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``` |
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2. Install the [Cityscapes developer kit](https://github.com/mcordts/cityscapesScripts) and build dependencies using `pip`. |
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```bash |
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python -m pip install -r requirements.txt |
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``` |
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3. Run the preparation script provided in this repository. |
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Note that this may prompt your [Cityscapes account](https://cityscapes-dataset.com/login/) login credentials. |
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```bash |
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make prepare |
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``` |
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4. To convert the `train`, `val` and `test` directories into a `tar` archive for use with [WebDataset](https://github.com/webdataset/webdataset), run the following command: |
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```bash |
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make build |
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``` |
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## Usage |
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See `examples.ipynb` for instructions. |
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## Citation |
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If you use this dataset in your research, please cite the original |
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[Cityscapes](https://cityscapes-dataset.com), |
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[Cityscapes-VPS](https://github.com/mcahny/vps), and |
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[Cityscapes-DVPS](https://github.com/joe-siyuan-qiao/ViP-DeepLab) datasets. |
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[^1]: Cordts et al., “The Cityscapes Dataset for Semantic Urban Scene Understanding” (CVPR 2016) |
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[^2]: Kim et al., "Video Panoptic Segmentation" (CVPR 2020) |
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[^3]: Qiao et al., "Learning Visual Perception with Depth-aware Video Panoptic Segmentation" (CVPR 2021) |
