The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation

Code

• github.com/SimJeg/FC-DenseNet
  OfficialIn papernone★ 0
• github.com/petko-nikolov/pysemseg
  pytorch★ 38
• github.com/IllIIIllll/where-is-wally
  tf★ 0
• github.com/kskim-phd/mfcn
  pytorch★ 0
• github.com/Vamshi399/CarND-Vehicle-Detection
  none★ 0
• github.com/pattyhendrix/CamVid-95-accuracy
  none★ 0
• github.com/0bserver07/One-Hundred-Layers-Tiramisu
  tf★ 0
• github.com/vivaan-park/where-is-wally
  tf★ 0
• github.com/bfortuner/pytorch_tiramisu
  pytorch★ 0
• github.com/SANKHA1/Vehicle-Detection
  none★ 0

Abstract

State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions. Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train. In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets. Code to reproduce the experiments is available here : https://github.com/SimJeg/FC-DenseNet/blob/master/train.py

Tasks

Benchmark Results

Reproductions