OSCAR: Occluded Stereo dataset for Convolutional Architectures with Recurrence

OSCAR, the Occluded Stereo dataset for Convolutional Architectures with Recurrence. Version: 2.0 (dataset as presented in our JOV 2021 journal publication "Recurrent Processing Improves Occluded Object Recognition and Gives Rise to Perceptual Hysteresis") If you make use of the dataset, please cite as follows: Ernst, M. R., Burwick, T., & Triesch, J. (2021). Recurrent Processing Improves Occluded Object Recognition and Gives Rise to Perceptual Hysteresis. In Journal of Vision 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-fmnist2c.zip - compressed archive of the occluded stereo FashionMNIST dataset (centered, ~1.1GB) os-fmnist2r.zip - compressed archive of the occluded stereo FashionMNIST dataset (random, ~1.2GB)  os-mnist2c.zip - compressed archive of the occluded stereo MNIST dataset (centered, ~865MB) os-mnist2r.zip - compressed archive of the occluded stereo MNIST dataset (random, ~851MB) os-ycb2.zip - compressed archive of the occluded stereo ycb-object dataset (~1.1GB) os-ycb2_highres.zip - compressed archive of the occluded stereo ycb-object dataset (high resolution, ~9.8GB) OSCARv2_dataset.py - python script to directly load image data from folder, pytorch dataset