Published April 22, 2020
| Version 1.0
Dataset
Open
OSCAR: Occluded Stereo dataset for Convolutional Architectures with Recurrence
Authors/Creators
- 1. Frankfurt Institute for Advanced Studies, Goethe-Universität Frankfurt
Description
OSCAR, the Occluded Stereo dataset for Convolutional Architectures with Recurrence. Version: 1.0
(dataset as presented in our ESANN 2020 conference publication "Recurrent Feedback Improves Recognition of Partially Occluded Objects")
If you make use of the dataset, please cite as follows:
Ernst M.R., Triesch J., Burwick T. (2020). Recurrent Feedback Improves Recognition of Partially Occluded Objects. In Proceedings of the 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Contents
- readme.md - detailed description and sample pictures
- img.zip - folder that contains images for the readme file
- licence.md - licence agreement for using the datasets
- os-mnist.zip - compressed archive of the occluded stereo multi-MNIST dataset (~1,7GB)
- os-ycb.zip - compressed archive of the occluded stereo ycb-object dataset (~1.1GB)
Files
img.zip
Files
(2.8 GB)
| Name | Size | |
|---|---|---|
|
md5:d13f6507f5a032caf2ccb3b4c6c1baf9
|
1.7 MB | Preview Download |
|
md5:2b9336d41c88b0cae00d5aab4b553b8c
|
2.2 kB | Preview Download |
|
md5:7079fb84d14ec07d3076e85c6660fadd
|
1.7 GB | Preview Download |
|
md5:bcdbe7076fef3a9152de219af1dc3640
|
1.1 GB | Preview Download |
|
md5:be71a00f1b67744a2dfeafdec82e96f6
|
5.0 kB | Preview Download |