데이터셋 탐색

Builds on top of recent data collection efforts by domain experts in these applications and provides a unified collection of datasets with evaluation metrics and train/test splits that are representative of real-world distribution shifts. The v2.0 update adds unlabeled data to 8 datasets. The labeled data and evaluation metrics are exactly the same, so all previous results are directly comparable. Source: WILDS: A Benchmark of in-the-Wild Distribution Shifts

WildScenes is a bi-modal benchmark dataset consisting of multiple large-scale, sequential traversals in natural environments, including semantic annotations in high-resolution 2D images and dense 3D LiDAR point clouds, and accurate 6-DoF pose information. The data is (1) trajectory-centric with accurate localization and globally aligned point clouds, (2) calibrated and synchronized to support bi-modal training and inference, and (3) containing different natural environments over 6 months to support research on domain adaptation. We introduce benchmarks on 2D and 3D semantic segmentation and evaluate a variety of recent deep-learning techniques to demonstrate the challenges in semantic segmentation in natural environments. We propose train-val-test splits for standard benchmarks as well as domain adaptation benchmarks and utilize an automated split generation technique to ensure the balance of class label distributions. The WildScenes benchmark webpage is https://csiro-robotics.github.io/WildScenes, and the data is publicly available at https://data.csiro.au/collection/csiro:61541 .

The iWildCam2020-WILDS dataset is a variant of the iWildCam 2020 dataset. iWildCam2020-WILDS is a benchmark dataset designed to test OOD generalization for the task of species classification. The label space consists of 182 species. Each domain corresponds to a different location of the camera trap. The training and test images belong to disjoint sets of locations in the OOD setting.