Oct 9, 2017 · In this paper, we propose a deep architecture that is able to run in real time while providing accurate semantic segmentation.
A toolbox that uses Torch library for training and evaluating the ERFNet architecture for semantic segmentation.
This paper proposes a light-weight stage-pooling semantic segmentation network (SPSSN), which can efficiently reuse the paramount features from early layers at ...
Oct 9, 2020 · Outperforms DilatedNet, DPN, FCN, DeepLabv1, ENet & SegNet, Similar accuracy to SOTA, RefineNet & DeepLabv2, While Taking Only 24ms Per Image on a Single GPU.
In this paper, we propose a deep architecture that is able to run in real time while providing accurate semantic segmentation. The core of our architecture is a ...
This code is a toolbox that uses PyTorch for training and evaluating the ERFNet architecture for semantic segmentation.
In this paper, we propose a deep architecture that is able to run in real-time while providing accurate semantic segmentation.
Missing: ERFNet: | Show results with:ERFNet:
This paper proposes a deep architecture that is able to run in real-time while providing accurate semantic segmentation and achieves a classification ...
A model for semantic segmentation that recently came out is the Efficient Residual Factorized Network (ERFNet)
Missing: ConvNet | Show results with:ConvNet
achieve accurate segmentation in real time. Our architecture is built in a sequential way by stacking. layers based on our novel redesign of the residual layer.