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https://github.com/lyft/nuscenes-devkit
Devkit for the public 2019 Lyft Level 5 AV Dataset (fork of https://github.com/nutonomy/nuscenes-devkit)
https://github.com/lyft/nuscenes-devkit
lyft
Last synced: about 1 month ago
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Devkit for the public 2019 Lyft Level 5 AV Dataset (fork of https://github.com/nutonomy/nuscenes-devkit)
- Host: GitHub
- URL: https://github.com/lyft/nuscenes-devkit
- Owner: lyft
- License: other
- Created: 2019-07-22T20:15:24.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-06-23T19:52:52.000Z (over 1 year ago)
- Last Synced: 2024-11-14T12:54:46.971Z (about 1 month ago)
- Topics: lyft
- Language: Jupyter Notebook
- Homepage:
- Size: 20.7 MB
- Stars: 375
- Watchers: 351
- Forks: 101
- Open Issues: 52
-
Metadata Files:
- Readme: README.md
- Contributing: docs/CONTRIBUTING.md
- License: license.txt
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README
# Lyft Dataset SDK
Welcome to the devkit for the [Lyft Level 5 AV dataset](https://level5.lyft.com/dataset/)! This devkit shall help you to visualise and explore our dataset.
## Release Notes
This devkit is based on a version of the [nuScenes devkit](https://www.nuscenes.org).
## Getting Started
### Installation
You can use pip to install [lyft-dataset-sdk](https://pypi.org/project/lyft-dataset-sdk/):
```bash
pip install -U lyft_dataset_sdk
```
If you want to get the latest version of the code before it is released on PyPI you can install the library from GitHub:
```bash
pip install -U git+https://github.com/lyft/nuscenes-devkit
```
### Dataset Download
Go to to download the Lyft Level 5 AV Dataset.
The dataset is also availible as a part of the [Lyft 3D Object Detection for Autonomous Vehicles Challenge](https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles).
### Utils for converting LEVEL5 data into Kitti format
Simply run
`python -m lyft_dataset_sdk.utils.export_kitti nuscenes_gt_to_kitti --lyft_dataroot ${DS_PTH} --table_folder ${TBL_PTH}`
for converting data.
See help ( `python -m lyft_dataset_sdk.utils.export_kitti nuscenes_gt_to_kitti --help` ) for more information.
You can draw results after converting with utils:
`python -m lyft_dataset_sdk.utils.export_kitti render_kitti`
### Tutorial and Reference Model
Check out the [tutorial and reference model README](notebooks/README.md).
# Dataset structure
The dataset contains of json files:
1. `scene.json` - 25-45 seconds snippet of a car's journey.
2. `sample.json` - An annotated snapshot of a scene at a particular timestamp.
3. `sample_data.json` - Data collected from a particular sensor.
4. `sample_annotation.json` - An annotated instance of an object within our interest.
5. `instance.json` - Enumeration of all object instance we observed.
6. `category.json` - Taxonomy of object categories (e.g. vehicle, human).
7. `attribute.json` - Property of an instance that can change while the category remains the same.
8. `visibility.json` - (currently not used)
9. `sensor.json` - A specific sensor type.
10. `calibrated_sensor.json` - Definition of a particular sensor as calibrated on a particular vehicle.
11. `ego_pose.json` - Ego vehicle poses at a particular timestamp.
12. `log.json` - Log information from which the data was extracted.
13. `map.json` - Map data that is stored as binary semantic masks from a top-down view.
With [the schema](schema.md).
# Data Exploration Tutorial
To get started with the Lyft Dataset SDK, run the tutorial using [Jupyter Notebook](notebooks/tutorial_lyft.ipynb).
# Contributing
We would be happy to accept issue reports and pull requests from the community.
For creating pull requests follow our [contributing guide](docs/CONTRIBUTING.md).