# DTS-Net: Depth-to-Space Networks for Fast and Accurate Semantic Object Segmentation
## 4 Experiments
### Benchmarks  
To train and test our approach, we used three popular benchmarks for semantic segmentation, PASCAL VOC2012, NYUV2 Depth, and Cityscapes. Two of these benchmarks contain depth data.  
**PASCAL VOC2012**[2] is a popular benchmark with 20 commonly seen classes. The dataset contains annotation for several computer vision tasks such as bounding-box object detection, semantic segmentation, instance segmentation, and action recognition. The segmentation data contain 2913 images of varying sizes: 1464 for training and 1449 for validation, the definition of the classes and the color used for each class' mask is shown in Figure 4a. We resized the images to 480 \(\times\) 480 and 256 \(\times\) 256 for DTS-NET and DTS-Net-Lite training and testing, respectively. We trained the network as detailed in the previous section and constructed the segmentation map with the DTS layer at the same size as the image. This was achieved using feature map sizes of 15 \(\times\) 15 and 8 \(\times\) 8, both with a depth of 1024, for DTS-Net and DTS-Net-Lite, respectively (both architectures down-sampled the image to 1/32 nd the input image size). In testing both networks, we used the same image size as in training.  
**NYUV2 Depth**[3] is a benchmark for semantic segmentation and depth estimation. The image data are in RGB-D format and captured using a Kinect sensor. The dataset contains 1449 images of 13 indoor object classes, that is, 795 and 654 images for training and testing, respectively, with fixed image sizes, the definition of the classes, and the color used for each class' mask is shown in Figure 4b. We trained and tested DTS-Net with the original image size (640 \(\times\) 480) and used a 320 \(\times\) 256 image size for DTS-Net-Lite. We trained both networks as described in Section III, with feature map sizes before the DTS layer of 20 \(\times\) 15 and 10 \(\times\) 8, both with a depth of 1024, for DTS-Net and DTS-Net-Lite, respectively.  
**Cityscapes**[4] is another benchmark for semantic segmentation and depth estimation, which contains urban street scenes in 50 cities, organized as 19 classes grouped into 8 categories. The dataset contains 5000 training images with fine segmentation masks, an additional 20,000 coarse annotated images, and 500 validation images, the definition of the classes and the color used for each class' mask is shown in Figure 4c. We trained our models with the 5000 fine annotated images as we required high-resolution segmentation and depth estimation with fine borders. The original size of the images in the dataset was \(2048\times 1024\). We resized the training and testing images to half this size, that is, to \(1024\times 512\). This was still a high resolution for DTS-Net but allowed considerably faster training and inference. We then validated the model with the validation data. For DTS-Net-Lite, we resized the images to approximately one-third of the original size, that is, to \(704\times 352\), for higher-speed processing while sacrificing some accuracy. The feature map sizes before the DTS layer were \(32\times 16\) and \(22\times 11\) with a depth of 1024 for DTS-Net and DTS-Net-Lite, respectively.