Shengliang Pu's starred repositories

scikit-learn

scikit-learn: machine learning in Python

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mmdetection

OpenMMLab Detection Toolbox and Benchmark

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segmentation_models.pytorch

Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.

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MNN

MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba

CenterNet

Object detection, 3D detection, and pose estimation using center point detection:

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segmentation_models

Segmentation models with pretrained backbones. Keras and TensorFlow Keras.

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shogun

Shōgun

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CenterNet

Codes for our paper "CenterNet: Keypoint Triplets for Object Detection" .

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awesome-slam-datasets

A curated list of awesome datasets for SLAM

thundersvm

ThunderSVM: A Fast SVM Library on GPUs and CPUs

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GCNet

GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond

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curve-gcn

Official PyTorch code for Curve-GCN (CVPR 2019)

thundergbm

ThunderGBM: Fast GBDTs and Random Forests on GPUs

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milk

MILK: Machine Learning Toolkit

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OctaveConv_pytorch

Pytorch implementation of newly added convolution

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OctaveConv

A MXNet Implementation for Drop an Octave

deepaugment

Discover augmentation strategies tailored for your dataset

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DeepWeeds

A Multiclass Weed Species Image Dataset for Deep Learning

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cellularAutomata

a collection of cellular automata written in Haskell with Diagrams

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VisDrone2018-DET-toolkit

Object Detection in Images toolkit for VisDrone2019

EarthMapper

Pipeline for the Semantic Segmentation (i.e., classification) of Remote Sensing Imagery

shogun-data

The Shogun Machine Learning Toolbox (Data Sets)

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urban-lu-model

A land use model based on Cellular Automata and Agent-based modelling.

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Low-Power-and-High-Speed-Deep-FPGA-Inference-Engines-for-Weed-Classification

IEEE Access: Low-Power and High-Speed Deep FPGA Inference Engines for Weed Classification at the Edge

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shogun-kaggle

Shogun applied to Kaggle tasks

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shogun-gpl

Optional GPL compatible parts of Shogun

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