## What files do I need?

You will need the LIDAR, image, map and data files for both train and test (`test_images.zip`, `test_lidar.zip`, etc.).  You may also need the `train.csv`, which includes the sample annotations in the form expected for submissions.  The `sample_submission.csv` file contains all of the sample `Id`s for the test set.

The data files (`test_data.zip`, `train_data.zip`) are in JSON format.

## What should I expect the data format to be?

The data comes in the form of many interlocking tables and formats.  The JSON files all contain single tables with identifying `tokens` that can be used to join with other files / tables. The images and lidar files all correspond to a sample in `sample_data.json`, and the `sample_token` from `sample_data.json` is the primary identifier used for the train and test samples.

The annotations in `train.csv` are in the following format:
`center_x center_y center_z width length height yaw class_name`

- `center_x`, `center_y` and `center_z` are the world coordinates of the center of the 3D bounding volume.
- `width`, `length` and `height` are the dimensions of the volume.
- `yaw` is the angle of the volume around the `z` axis (where `y` is forward/back, `x` is left/right, and `z` is up/down - making 'yaw' the direction the front of the vehicle / bounding box is pointing at while on the ground).
- `class_name` is the type of object contained by the bounding volume.

## What am I predicting?

For the test samples, we're predicting the bounding volumes and classes of all of the objects in a given scene.

For example, in `sample_token` `97ce3ab08ccbc0baae0267cbf8d4da947e1f11ae1dbcb80c3f4408784cd9170c`, we might be predicting the presence of a car (with `confidence` `1.0`) and a bus (with `confidence` `0.5`). The prediction would look like this:

    97ce3ab08ccbc0baae0267cbf8d4da947e1f11ae1dbcb80c3f4408784cd9170c,1.0 2742.152625996093 673.1631800662494 -18.6561112411676 1.834 4.609 1.648 2.619835541569646 car 0.5 2728.9634555684484 657.8296521874645 -18.54676216218047 1.799 4.348 1.728 -0.5425527100619654 bus

With all predicted objects on the same line.

Note that `confidence` values are inserted prior to `center_x center_y center_z width length height yaw class_name`.

## Files

*   **train_data.zip** and **test_data.zip** - contains JSON files with multiple tables. The most important is `sample_data.json`, which contains the primary identifiers used in the competition, as well as links to key image / lidar information.
*   **train_images.zip** and **test_images.zip** - contains .jpeg files corresponding to samples in `sample_data.json`
*   **train_lidar.zip** and **test_lidar.zip** - contains .jpeg files corresponding to samples in `sample_data.json`
*   **train_maps.zip** and **test_maps.zip** - contains maps of the entire sample area.
*   **train.csv** - contains all `sample_token`s in the train set, as well as annotations in the required format for all train set objects.
*   **sample_submission.csv** - contains all `sample_token`s in the test set, with empty predictions.