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automated-deep-photo-style-transfer

TensorFlow implementation for the paper "Automated Deep Photo Style Transfer"

awesome-semantic-segmentation-pytorch

Semantic Segmentation on PyTorch (include FCN, PSPNet, Deeplabv3, Deeplabv3+, DANet, DenseASPP, BiSeNet, EncNet, DUNet, ICNet, ENet, OCNet, CCNet, PSANet, CGNet, ESPNet, LEDNet, DFANet)

carnd-semantic-segmentation

crosswalk-detection

Smartphone-based crosswalk detection and localization for visually impaired pedestrians

deep-photo-style-transfer

deep-photo-style-transfer-1

Machine Learning and Deep Learning

deep-photo-style-transfer-tf-windows-python3

deep-photo-styletransfer-tf

Tensorflow (Python API) implementation of Deep Photo Style Transfer

deep-photo-styletransfer-tf_win

Windows Tensorflow(Python API) implementation of Deep Photo Style Transfer

deepphotostyle_pytorch

PyTorch implementation of "Deep Photo Style Transfer"

fastphotostyle

Style transfer, deep learning, feature transform

fcn-tensorflow-win10

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

fcn.tensorflow

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

hrnet-semantic-segmentation

The OCR approach is rephrased as Segmentation Transformer: https://arxiv.org/abs/1909.11065. This is an official implementation of semantic segmentation for HRNet. https://arxiv.org/abs/1908.07919

hrnet-w48-pytorch-windows

This is an unofficial implementation of semantic segmentation for TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition".

indoor-localization-with-imu

Pedestrian Dead Reckoning a.k.a. Indoor Localization using IMU (Inertial Measurement Unit)

keras-semantic-segmentation

Keras-Semantic-Segmentation

mask-r-cnn

Explaining the differences between traditional image classification, object detection, semantic segmentation, and instance segmentation is best done visually. When performing traditional image classification our goal is to predict a set of labels to characterize the contents of an input image (top-left). Object detection builds on image classification, but this time allows us to localize each object in an image. The image is now characterized by: Bounding box (x, y)-coordinates for each object An associated class label for each bounding box.Instance segmentation algorithms, on the other hand, compute a pixel-wise mask for every object in the image, even if the objects are of the same class label (bottom-right). Here you can see that each of the cubes has their own unique color, implying that our instance segmentation algorithm not only localized each individual cube but predicted their boundaries as well.

mask-r-cnn-based-road-semantic-segmentation

mmsegmentation

OpenMMLab Semantic Segmentation Toolbox and Benchmark.

pedestrian-ins-with-map-matching

Taha

pedestriannavigationprogram

release-and-smile

learn communicate and share. A better tomorrow ! A better self!

semantic-segmentation

Nvidia Semantic Segmentation monorepo

semantic-segmentation-1

I will upload many semantic segmentation models to this repository for you to learn

semantic-segmentation-pytorch

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

semantic-segmentation-suite

Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!

semseg

Semantic Segmentation in Pytorch

step-and-heading-indoor-localization

contains matlab code to determine location of a pedestrian in the built indoor environment through the use of Inertial measurement unit signal from a smartphone and a wrist based sensor, in combination with a particle filter

tfsegmentation

RTSeg: Real-time Semantic Segmentation Comparative Study