Chair Segments: A Compact Benchmark for the Study of Object Segmentation
This dataset provides a fast prototyping environment for researchers and developers working on object segmentation models, potentially accelerating algorithm design.
This paper introduces ChairSegments, a compact semi-synthetic dataset for object segmentation, designed to facilitate rapid iteration on model architectures. It enables a U-Net model to reach full convergence in 30 minutes on a single GPU and achieves state-of-the-art accuracy on the Object Discovery dataset when used for pretraining.
Over the years, datasets and benchmarks have had an outsized influence on the design of novel algorithms. In this paper, we introduce ChairSegments, a novel and compact semi-synthetic dataset for object segmentation. We also show empirical findings in transfer learning that mirror recent findings for image classification. We particularly show that models that are fine-tuned from a pretrained set of weights lie in the same basin of the optimization landscape. ChairSegments consists of a diverse set of prototypical images of chairs with transparent backgrounds composited into a diverse array of backgrounds. We aim for ChairSegments to be the equivalent of the CIFAR-10 dataset but for quickly designing and iterating over novel model architectures for segmentation. On Chair Segments, a U-Net model can be trained to full convergence in only thirty minutes using a single GPU. Finally, while this dataset is semi-synthetic, it can be a useful proxy for real data, leading to state-of-the-art accuracy on the Object Discovery dataset when used as a source of pretraining.