---
annotations_creators: []
language: en
license: bsd
task_categories:
- image-classification
- object-detection
task_ids: []
pretty_name: bdd100k-validation
tags:
- fiftyone
- image
- image-classification
- object-detection
dataset_summary: '



  ![image/png](dataset_preview.gif)



  This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 10000 samples.


  ## Installation


  If you haven''t already, install FiftyOne:


  ```bash

  pip install -U fiftyone

  ```


  ## Usage


  ```python

  import fiftyone as fo

  import fiftyone.utils.huggingface as fouh


  # Load the dataset

  # Note: other available arguments include ''split'', ''max_samples'', etc

  dataset = fouh.load_from_hub("dgural/bdd100k")


  # Launch the App

  session = fo.launch_app(dataset)

  ```

  '
---

# Dataset Card for bdd100k-validation


From one of the largest open source driving datasets, [BDD100k](https://www.vis.xyz/bdd100k/), is the BDD100K images dataset.
The dataset consists of every 10th second in the videos and contains a train, validation and test split.
It contains labels for object detection, weather, time of day, and scene of the driving!


![image/png](dataset_preview.gif)


This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 10000 samples.

## Installation

If you haven't already, install FiftyOne:

```bash
pip install -U fiftyone
```

## Usage

```python
import fiftyone as fo
import fiftyone.utils.huggingface as fouh

# Load the dataset
# Note: other available arguments include 'split', 'max_samples', etc
dataset = fouh.load_from_hub("dgural/bdd100k")

# Launch the App
session = fo.launch_app(dataset)
```


## Dataset Details

### Dataset Description


- **Curated by:** [ETH VIS Group](https://www.vis.xyz/)
- **Language(s) (NLP):** en
- **License:** bsd

### Dataset Sources [optional]


- **Repository:** [bdd100k](https://github.com/bdd100k/bdd100k)
- **Paper :** [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://arxiv.org/abs/1805.04687)

## Uses

By downoading, you are agreeing to the [BDD100K License](https://doc.bdd100k.com/license.html#license). 

### Direct Use

This dataset is great for self driving car applications, especially for dealing with many different weather conditions and different times of day!


## Dataset Structure
```
Name:        bdd100k-validation
Media type:  image
Num samples: 10000
Persistent:  True
Tags:        []
Sample fields:
    id:         fiftyone.core.fields.ObjectIdField
    filepath:   fiftyone.core.fields.StringField
    tags:       fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)
    metadata:   fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)
    weather:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
    timeofday:  fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
    scene:      fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classification)
    detections: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)
```
    



### Source Data

The dataset is sourced from [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://arxiv.org/abs/1805.04687)


#### Who are the source data producers?

BDD100K is now managed by the [ETH VIS Group](https://www.vis.xyz/bdd100k/)


## Citation

**BibTeX:**

@misc{yu2020bdd100k,
      title={BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning}, 
      author={Fisher Yu and Haofeng Chen and Xin Wang and Wenqi Xian and Yingying Chen and Fangchen Liu and Vashisht Madhavan and Trevor Darrell},
      year={2020},
      eprint={1805.04687},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}