Fully Convolutional Networks for Semantic Segmentation

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Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed the state-of-the-art in semantic segmentation. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning. We define and detail the space of fully convolutional networks, explain their application to spatially dense prediction tasks, and draw connections to prior models. We adapt contemporary classification networks (AlexNet, the VGG net, and GoogLeNet) into fully convolutional networks and transfer their learned representations by fine-tuning to the segmentation task. We then define a novel architecture that combines semantic information from a deep, coarse layer with appearance information from a shallow, fine layer to produce accurate and detailed segmentations. Our fully convolutional network achieves state-of-the-art segmentation of PASCAL VOC (20% relative improvement to 62.2% mean IU on 2012), NYUDv2, and SIFT Flow, while inference takes one third of a second for a typical image.

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Fully Convolutional Network for Semantic Segmentation

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github.com: /geodekid/FCN

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Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)

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A tensorflow implementation of Fully Convolutional Networks For Semantic Segmentation

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Image Segmentation and Object Detection in Pytorch

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semantic image segmentation networks implemented in tensorflow

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Run Long and Shelhamer's FCN image segmentation network using Caffe

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MIT-Princeton Vision Toolbox for the Amazon Picking Challenge 2016 - RGB-D ConvNet-based object segmentation and 6D object pose estimation.

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Pytorch implementation of "Fully Convolutional Networks for Semantic Segmentation"

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Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation

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Implementation of the paper Fully Convolutional Networks for Semantic Segmentation, used to segment a person from a background in an RGB image

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Test of the Fully Convolutional Network for semantic segmentation

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VOC2012 SegmentationClass with pretrain vgg16 model

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Fully Convolutional Networks for Semantic Segmentation by Jonathan Long, Evan Shelhamer, and Trevor Darrell. CVPR 2015 and PAMI 2016.

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  • CAMP 10 TensorFlow
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Testing fully convolutional networks for semantic segmentation with caffe for the cityscapes dataset

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Implement Fully Convolutional Networks for semantic segmentation to detect objects in an image.

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Code for several state-of-the-art papers in object detection and semantic segmentation.

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github.com: /TianchengQ/FCN

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week10

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github.com: /tsixta/jnet

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J-Net: Multiresolution Neural Network for Semantic Segmentation

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FCN8s

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Holed Convolution Layer for Semantic Segmentation in MatConvNet

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Semantic Segmentation Models in Pytorch

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github.com: /LeeMax117/FCN_8s

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FCN8s

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github.com: /zhuyi55/week10

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github.com: /fmahoudeau/fcn

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FCN for Semantic Image Segmentation on Tensorflow

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Fully Convolutional Networks for Semantic Segmentation

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segmentation

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Evaluation metrics for image segmentation inspired by paper Fully Convolutional Networks for Semantic Segmentation

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Image Segmentation framework based on Tensorflow and TF-Slim library

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GoogLeNet implementation of Fully Convolutional Networks for Semantic Segmentation in TensorFlow

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使用Pascal2 VOC2012的数据中,语义分割部分的数据作为作业的数据集,构建一个FCN训练模型进行练习

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15 CVPR(best mention) 875 Fully Convolutional Networks for Semantic Segmentation

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github.com: /yeLer/fcn

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The code revised from Fully Convolutional Networks for Semantic Segmentation

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Keras-tensorflow implementation of Fully Convolutional Networks for Semantic SegmentationUnfinished

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PyTorch for Semantic Segmentation

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Semantic segmentation to detect roads using fully convolutional neural networks

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Fully convolutional networks for semantic segmentation

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