Welcome to the devkit for the Lyft Level 5 AV dataset! This devkit shall help you to visualise and explore our dataset.
This devkit is based on a version of the nuScenes devkit.
You can use pip to install lyft-dataset-sdk:
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:
pip install -U git+https://github.com/lyft/nuscenes-devkit
Go to https://level5.lyft.com/dataset/ 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.
Check out the tutorial and reference model README.
The dataset contains of json files:
scene.json
- 25-45 seconds snippet of a car's journey.sample.json
- An annotated snapshot of a scene at a particular timestamp.sample_data.json
- Data collected from a particular sensor.sample_annotation.json
- An annotated instance of an object within our interest.instance.json
- Enumeration of all object instance we observed.category.json
- Taxonomy of object categories (e.g. vehicle, human).attribute.json
- Property of an instance that can change while the category remains the same.visibility.json
- (currently not used)sensor.json
- A specific sensor type.calibrated_sensor.json
- Definition of a particular sensor as calibrated on a particular vehicle.ego_pose.json
- Ego vehicle poses at a particular timestamp.log.json
- Log information from which the data was extracted.map.json
- Map data that is stored as binary semantic masks from a top-down view.
With the schema.
To get started with the Lyft Dataset SDK, run the tutorial using Jupyter Notebook.