Abstract Convolutional networks(ConvNets)have become the dominant approach to semantic image segmentation.Pro- ducing accurate,pixel-level labels required for this task is a tedious and time consuming process;however,producing approximate,coarse labels could take only a fraction cf the time and eifort.We investigate the relationship between the qualily cf labels and the per formance cf ConvNets for semantic segmentation.We create a very large synthetic dataset with peifeclly labeled street view scenes.From these peifect labels,we synthetically coarsen labels with djferent qualities and estimate human-hours required for producing them.We per form a series cf experiments by training Con- vNets with a varying number cf training images and label quality.We found that the performance cf ConvNets mostly depends on the time spent creating the training labels.That is,a larger coarsely-annotated dataset can yield the same performance as a smaller finely-annotated one.Further- more,fine-tuning coarsely pre-trained ConvNets with few finely-annotated labels can yield comparable or superior pei formance to training it with a large amount cf finely- annotated labels alone,at a fraction cf the labeling cost.We demonstrate that our result is also valid for di;ferent network architectures,and various ot ject classes in an urban scene