郭肖亭's repositories
mmsegmentation
OpenMMLab Semantic Segmentation Toolbox and Benchmark.
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)
HRNet-W48-Pytorch-Windows
This is an unofficial implementation of semantic segmentation for TPAMI paper "Deep High-Resolution Representation Learning for Visual Recognition".
TFSegmentation
RTSeg: Real-time Semantic Segmentation Comparative Study
semantic-segmentation-pytorch
Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset
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
Semantic-Segmentation-Suite
Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!
semantic-segmentation
Nvidia Semantic Segmentation monorepo
semseg
Semantic Segmentation in Pytorch
Semantic-Segmentation-1
I will upload many semantic segmentation models to this repository for you to learn
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
Keras-Semantic-Segmentation
Keras-Semantic-Segmentation
crosswalk-detection
Smartphone-based crosswalk detection and localization for visually impaired pedestrians
FastPhotoStyle
Style transfer, deep learning, feature transform
FCN.tensorflow
Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)
Indoor-Localization-WIth-IMU
Pedestrian Dead Reckoning a.k.a. Indoor Localization using IMU (Inertial Measurement Unit)
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.
DeepPhotoStyle_pytorch
PyTorch implementation of "Deep Photo Style Transfer"
automated-deep-photo-style-transfer
TensorFlow implementation for the paper "Automated Deep Photo Style Transfer"
Deep-Photo-Style-Transfer-1
Machine Learning and Deep Learning
FCN-tensorflow-win10
Tensorflow implementation of Fully Convolutional Networks for Semantic Segmentation (http://fcn.berkeleyvision.org)
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
release-and-smile
learn communicate and share. A better tomorrow ! A better self!