论文标题
椅子细分：用于研究对象细分的紧凑基准
Chair Segments: A Compact Benchmark for the Study of Object Segmentation
论文作者
论文摘要
多年来，数据集和基准对新算法的设计产生了巨大影响。在本文中，我们介绍了一个新颖而紧凑的半合成数据集，用于对象分割。我们还显示了转移学习中的经验发现，这些发现反映了图像分类的最新发现。我们特别表明，在优化景观的相同盆地中，根据一组重量进行微调的模型。椅子店由一套透明背景的椅子的各种典型图像组成，这些椅子合成各种背景。我们的目的是使椅子与CIFAR-10数据集相当，但要快速设计和迭代新颖的模型体系结构以进行细分。在椅子段上，可以使用单个GPU在三十分钟内训练U-NET型号，以完全收敛。最后，尽管该数据集是半合成的，但它可能是真实数据的有用代理，当将其用作预读源时，导致对象发现数据集的最新精度。
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.
