Real-time semantic segmentation is the task of achieving computationally efficient semantic segmentation (while maintaining a base level of accuracy).
( Image credit: TorchSeg )
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We show that SegNet provides good performance with competitive inference time and more efficient inference memory-wise as compared to other architectures.
Ranked #3 on Scene Segmentation on SUN-RGBD
Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4. 4% higher accuracy with 6x fewer FLOPs.
Ranked #26 on Object Detection on PASCAL VOC 2007
Semantic segmentation requires both rich spatial information and sizeable receptive field.
Ranked #4 on Semantic Segmentation on SkyScapes-Dense
We focus on the challenging task of real-time semantic segmentation in this paper.
Ranked #9 on Real-Time Semantic Segmentation on CamVid
Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding.
Ranked #18 on Real-Time Semantic Segmentation on Cityscapes test
The Jaccard index, also referred to as the intersection-over-union score, is commonly employed in the evaluation of image segmentation results given its perceptual qualities, scale invariance - which lends appropriate relevance to small objects, and appropriate counting of false negatives, in comparison to per-pixel losses.
Ranked #17 on Real-Time Semantic Segmentation on Cityscapes test