# BDD100K
The Berkeley Deep Drive (BDD) dataset is one of the largest and most diverse
video datasets for autonomous vehicles.
The BDD100K dataset contains 100,000 video clips collected from more than
50,000 rides covering New York, San Francisco Bay Area, and other regions.
The dataset contains diverse scene types such as city streets, residential
areas, and highways. Furthermore, the videos were recorded in diverse
weather conditions at different times of the day.
The videos are split into training (70K), validation (10K) and testing
(20K) sets. Each video is 40 seconds long with 720p resolution and a frame
rate of 30fps. The frame at the 10th second of each video is annotated for
image classification, detection, and segmentation tasks.
This version of the dataset contains only the 100K images extracted from
the videos as described above, together with the image classification,
detection, and segmentation labels.
#### NOTE
In order to load the BDD100K dataset, you must download the source data
manually. The directory should be organized in the following format:
```text
source_dir/
labels/
bdd100k_labels_images_train.json
bdd100k_labels_images_val.json
images/
100k/
train/
test/
val/
```
You can register at [http://bdd-data.berkeley.edu](http://bdd-data.berkeley.edu) in order to get links
to download the data.
**Details**
- Dataset name: `bdd100k`
- Dataset source: [http://bdd-data.berkeley.edu](http://bdd-data.berkeley.edu)
- Dataset license: [http://bdd-data.berkeley.edu/download.html](http://bdd-data.berkeley.edu/download.html)
- Dataset size: 7.10 GB
- Tags: `image, multilabel, automotive, manual`
- Supported splits: `train, validation, test`
- ZooDataset class:
[`BDD100KDataset`](../../api/fiftyone.zoo.datasets.base.md#fiftyone.zoo.datasets.base.BDD100KDataset)
**Example usage**
Python
CLI
```python
import fiftyone as fo
import fiftyone.zoo as foz
# The path to the source files that you manually downloaded
source_dir = "/path/to/dir-with-bdd100k-files"
dataset = foz.load_zoo_dataset(
"bdd100k",
split="validation",
source_dir=source_dir,
)
session = fo.launch_app(dataset)
```
```shell
# The path to the source files that you manually downloaded
SOURCE_DIR="/path/to/dir-with-bdd100k-files"
fiftyone zoo datasets load bdd100k --split validation \
--kwargs "source_dir=${SOURCE_DIR}"
fiftyone app launch bdd100k-validation
```
