# High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks Source: [High-performance Semantic Segmentation Using Very Deep Fully Convolutional Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:19:32.265Z Authors: Zifeng Wu, Chunhua Shen, Anton van den Hengel Published: 2016-04-15T02:52:46.000Z ## Abstract We propose a method for high-performance semantic image segmentation (or semantic pixel labelling) based on very deep residual networks, which achieves the state-of-the-art performance. A few design factors are carefully considered to this end. We make the following contributions. (i) First, we evaluate different variations of a fully convolutional residual network so as to find the best configuration, including the number of layers, the resolution of feature maps, and the size of field-of-view. Our experiments show that further enlarging the field-of-view and increasing the resolution of feature maps are typically beneficial, which however inevitably leads to a higher demand for GPU memories. To walk around the limitation, we propose a new method to simulate a high resolution network with a low resolution network, which can be applied during training and/or testing. (ii) Second, we propose an online bootstrapping method for training. We demonstrate that online bootstrapping is critically important for achieving good accuracy. (iii) Third we apply the traditional dropout to some of the residual blocks, which further improves the performance. (iv) Finally, our method achieves the currently best mean intersection-over-union 78.3\\% on the PASCAL VOC 2012 dataset, as well as on the recent dataset Cityscapes. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on PASCAL VOC 2012 (test)](): Semantic segmentation; PASCAL VOC 2012 (test); mIoU: 79.1. ## Original sources and implementation [Original paper]() [PDF]()