Dataset for "Invariance of Object Detection in Untrained Deep Neural Networks"

Dataset for
"Invariance of Object Detection in Untrained Deep Neural Networks"
Jeonghwan Cheon, Seungdae Baek, and Se-Bum Paik*
*Contact: [e-mail]

To run demo codes for "Invariance of Object Detection in Untrained Deep Neural Networks", please download files below.

1. Image.zip
- Object dataset (Foldername: selectivity_var): This set was used to find units that selectively respond to a specific object class. It contains nine object classes (bed, chair, desk, dresser, nightstand, monitor, sofa, table, toilet) and 200 images are prepared to an object class. Each image has different object identities, which means it rendered from different object 3D models (Princeton ModelNet, a 3D CAD model dataset for computer vision and cognitive science [https://modelnet.cs.princeton.edu/]). To render image of object dataset, horizontal viewpoint variation angle was randomly set between -30° and +30°. In object dataset, brightness and contrast of images are statistically comparable across the object class.
- Viewpoint dataset for invariance test (Folder name: invariance_test): This set was used to test the viewpoint invariant characteristic of object selective units. This dataset consists of 13 subsets which has different viewpoints from -180° to +180° in linear scale step. It contains 200 different object identities in an object class, which are the same as those used in the object dataset.
- Viewpoint dataset for finding invariant unit (Folder name: invariance_unit): This set was used to find object selective units that specifically or invariantly responded to object images of different viewpoints. This dataset consists of five angle-based viewpoint classes (-60°, -30°, 0°, 30°, 60°) with 50 object identities which were not used to find object selective unit
- SVM dataset (Folder name: SVM_var): This set was used to train and test SVM which performs object detection task. It contains 60 different object identities in an object class, which were not used to find object selective unit. Specifically, it consists of 18 subsets which has different viewpoint variation range from 0° to 180°. For example, subset with 180° viewpoint variation range contains images which shows different viewpoints of objects within range of -90° and +90°.