Baselines and a datasheet for the Cerema AWP dataset
By Ismaïla Seck, Khouloud Dahmane, Pierre Duthon, Gaëlle Loosli. 2018
12 citations recorded by the source corpus.
This paper presents the recently published Cerema AWP (Adverse Weather Pedestrian) dataset for various machine learning tasks and its exports in machine learning friendly format. We explain why this dataset can be interesting (mainly because it is a greatly controlled and fully annotated image dataset) and present baseline results for various tasks. Moreover, we decided to follow the very recent suggestions of datasheets for dataset, trying to standardize all the available information of the dataset, with a transparency objective.
Subfields: Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Evaluation & Benchmarks
- Jennifer Wortman Vaughan
- Alexander Wong
- Jamie H. Morgenstern
- Hal Daumé Iii
- Hanna M. Wallach
- Timnit Gebru
- Chris Dulhanty
- Emily L. Denton
Citations in the corpus
- Datasheets for datasets (2018, background): “[21] Ismaïla Seck, Khouloud Dahmane, Pierre Duthon, and Gaëlle Loosli.”
- Pitfalls in Machine Learning Research: Reexamining the Development Cycle (2020, unknown): “From the point of view of documentation, the “Datasheets for Datasets” [21] framework is something that people are beginning to use and which would benefit the field if adopted as a standard [43, 15, 47].”
- Between Subjectivity and Imposition (2020, unknown): “In line with previous literature [7, 38, 49, 74, 78], we advocate for the documentation of purpose, composition, and intention of datasets.”
- Auditing ImageNet: Towards a Model-driven Framework for Annotating Demographic Attributes of Large-Scale Image Datasets (2019, background): “Since the initial release of this paper, several datasets have adopted this practice [27, 24], however work remains for wide adoption.”