Method Details
Details for method 'DeepLabv2-CRF'
Method overview
| name | DeepLabv2-CRF |
| challenge | pixel-level semantic labeling |
| details | DeepLabv2-CRF is based on three main methods. First, we employ convolution with upsampled filters, or ‘atrous convolution’, as a powerful tool to repurpose ResNet-101 (trained on image classification task) in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within DCNNs. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and fully connected Conditional Random Fields (CRFs). The model is only trained on train set. |
| publication | DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille arXiv preprint http://arxiv.org/abs/1606.00915 |
| project page / code | http://liangchiehchen.com/projects/DeepLab.html |
| used Cityscapes data | fine annotations |
| used external data | ImageNet |
| runtime | n/a |
| subsampling | no |
| submission date | May, 2016 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| IoU Classes | 70.3816 |
| iIoU Classes | 42.5971 |
| IoU Categories | 86.4017 |
| iIoU Categories | 67.696 |
Class results
| Class | IoU | iIoU |
|---|---|---|
| road | 97.8649 | - |
| sidewalk | 81.3219 | - |
| building | 90.35 | - |
| wall | 48.7736 | - |
| fence | 47.3634 | - |
| pole | 49.5789 | - |
| traffic light | 57.8685 | - |
| traffic sign | 67.2847 | - |
| vegetation | 91.8508 | - |
| terrain | 69.4396 | - |
| sky | 94.192 | - |
| person | 79.8312 | 51.4918 |
| rider | 59.8495 | 31.2131 |
| car | 93.7134 | 85.3921 |
| truck | 56.5019 | 26.5142 |
| bus | 67.4976 | 37.8275 |
| train | 57.4574 | 34.4958 |
| motorcycle | 57.6633 | 27.3617 |
| bicycle | 68.8479 | 46.4802 |
Category results
| Category | IoU | iIoU |
|---|---|---|
| flat | 98.2689 | - |
| nature | 91.4789 | - |
| object | 57.2914 | - |
| sky | 94.192 | - |
| construction | 90.7687 | - |
| human | 80.2267 | 52.5053 |
| vehicle | 92.5854 | 82.8867 |