metadata

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, 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!

This is a FiftyOne dataset with 10000 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

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
Language(s) (NLP): en
License: bsd

Dataset Sources [optional]

Repository: bdd100k
Paper : BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

Uses

By downoading, you are agreeing to the BDD100K 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

Who are the source data producers?

BDD100K is now managed by the ETH VIS Group

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} }