---
license: cc-by-nc-sa-4.0
---

# OpenDV-YouTube

This is the dataset repository of `OpenDV-YouTube` language annotations, including `context` and `command`. For more details, please refer to <a href="https://arxiv.org/abs/2403.09630" target="_blank">GenAD</a> project and <a href="https://github.com/OpenDriveLab/DriveAGI#opendv-youtube" target="_blank">OpenDV-YouTube</a>.

## Usage

To use the annotations, you need to first download and prepare the data as instructed in <a href="https://github.com/OpenDriveLab/DriveAGI/tree/main/opendv" target="_blank">OpenDV-YouTube</a>. **Note that we recommend to process the dataset in `Linux` environment since `Windows` may have issues with the file paths.**

You can use the following code to load in annotations respectively.

```python
import json

# for train
full_annos = []
for split_id in range(10):
  split = json.load(open("10hz_YouTube_train_split{}.json".format(str(split_id)), "r"))
  full_annos.extend(split)

# for val
val_annos = json.load(open("10hz_YouTube_val.json", "r"))
```

Annotations will be loaded in `full_annos` as a list where each element contains annotations for one video clip. All elements in the list are dictionaries of the following structure.

```python
{
  "cmd": <int> -- command, i.e. the command of the ego vehicle in the video clip.
  "blip": <str> -- context, i.e. the BLIP description of the center frame in the video clip.
  "folder": <str> -- the relative path from the processed OpenDV-YouTube dataset root to the image folder of the video clip.
  "first_frame": <str> -- the filename of the first frame in the clip. Note that this file is included in the video clip.
  "last_frame": <str> -- the filename of the last frame in the clip. Note that this file is included in the video clip.
}
```

The command, *i.e.* the `cmd` field, can be converted to natural language using the `map_category_to_caption` function. You may refer to [cmd2caption.py](https://github.com/OpenDriveLab/DriveAGI/blob/main/opendv/utils/cmd2caption.py#L158) for details.

The context, *i.e.* the `blip` field, is the description of the **center frame** in the video generated by `BLIP2`.


## Citation

If you find our work helpful, please cite the following paper.

```bibtex
@misc{yang2024genad,
      title={Generalized Predictive Model for Autonomous Driving}, 
      author={Jiazhi Yang and Shenyuan Gao and Yihang Qiu and Li Chen and Tianyu Li and Bo Dai and Kashyap Chitta and Penghao Wu and Jia Zeng and Ping Luo and Jun Zhang and Andreas Geiger and Yu Qiao and Hongyang Li},
      year={2024},
      eprint={2403.09630},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```