Metadata-Version: 2.1
Name: bdd100k
Version: 1.0.0
Summary: BDD100K Dataset Toolkit
Home-page: https://www.bdd100k.com/
Author: Fisher Yu
Author-email: i@yf.io
License: UNKNOWN
Project-URL: Documentation, https://doc.bdd100k.com/
Project-URL: Source, https://github.com/bdd100k/bdd100k
Project-URL: Tracker, https://github.com/bdd100k/bdd100k/issues
Description: <p align="center"><img width=300 src="https://bdd100k.com/images/bdd100k-logo.svg" /></p>
        
        ---
        
        BDD100K is a diverse driving dataset for heterogeneous multitask learning.
        
        [**Homepage**](https://www.bdd100k.com/) |
        [**Paper**](https://arxiv.org/abs/1805.04687) |
        [**Doc**](https://doc.bdd100k.com) |
        [**Questions**](https://groups.google.com/d/forum/bdd100k-discuss)
        
        ![teaser](doc/images/teaser.gif)
        
        We construct BDD100K, the largest open driving video dataset with 100K videos
        and 10 tasks to evaluate the exciting progress of image recognition algorithms
        on autonomous driving. Each video has 40 seconds and a high resolution. The
        dataset represents more than 1000 hours of driving experience with more than 100
        million frames. The videos comes with GPU/IMU data for trajectory information.
        The dataset possesses geographic, environmental, and weather diversity, which is
        useful for training models that are less likely to be surprised by new
        conditions. The dynamic outdoor scenes and complicated ego-vehicle motion make
        the perception tasks even more challenging. The tasks on this dataset include
        image tagging, lane detection, drivable area segmentation, road object
        detection, semantic segmentation, instance segmentation, multi-object detection
        tracking, multi-object segmentation tracking, domain adaptation, and imitation
        learning. This repo contains the toolkit and resources for using [BDD100K
        data](https://arxiv.org/abs/1805.04687). To cite the dataset in your paper,
        
        ```
        @InProceedings{bdd100k,
            author = {Yu, Fisher and Chen, Haofeng and Wang, Xin and Xian, Wenqi and Chen,
                      Yingying and Liu, Fangchen and Madhavan, Vashisht and Darrell, Trevor},
            title = {BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning},
            booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
            month = {June},
            year = {2020}
        }
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
