Stars
Toolbox for spectral non-parametric clustering of SPD matices (covariance matrices and ellipsoids). Also contains code for EM-based GMM learning and inference for Bayesian non-parametric CRP-GMM.
Please choose the openseg.pytorch project for the updated code that achieve SOTA on 6 benchmarks!
A wait-free parallel implementation of the union-find data structure for CPUs and GPUs.
CUDA-accelerated minimum spanning tree algorithm -- data parallel Boruvka's algorithm
GPU based fast MST using CUDA
High Performance Computing on Graphics Processing Units, Mean Shift Image Segmentation
OpenMMLab Detection Toolbox and Benchmark
UNet model with VGG11 encoder pre-trained on Kaggle Carvana dataset
Hyperspectral-Classification Pytorch
[CVPR2019] Fast Online Object Tracking and Segmentation: A Unifying Approach
Codes for paper "Mask Scoring R-CNN".
Superpixel segmentation using agglomerative clustering implemented as a VTK filter. Finds a globally optimal segmentation.
Image Segmentation using Superpixels, Affinity Propagation and Kmeans Clustering
R-FCN: Object Detection via Region-based Fully Convolutional Networks
Fully Convolutional Instance-aware Semantic Segmentation
segmentation, instance segmentation and single image depth
Pytorch implementation of refinenet network
code for "Adaptive Fusion for RGB-D Salient Object Detection"
text detection mainly based on ctpn model in tensorflow, id card detect, connectionist text proposal network
Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!
Pytorch Implementation of Refinenet
The work I have done for pelvic organ segmentation, implemented by pytorch.
基于v2版本的deeplab,使用VGG16模型，在VOC2012，Pascal-context，NYU-v2等多个数据集上进行训练