Lizhanbin (lzb863)

lzb863

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Location:shenzhen.china

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Lizhanbin's repositories

WeightWatcher

The WeightWatcher tool for predicting the accuracy of Deep Neural Networks

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Awesome-Pruning

A curated list of neural network pruning resources.

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ESPNet

Code for ESPNet(Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation) in Tensorflow.

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VINS-Mono-Learning

VINS-Mono代码注释,仅供学习

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irn

Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations, CVPR 2019 (Oral)

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Codes-for-Lane-Detection

Tensorflow implementation of "Spatial As Deep: Spatial CNN for Traffic Scene Understanding" (AAAI 2018)

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semseg

常用的语义分割架构结构综述以及代码复现

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loop

Self-Driving Car by Aaron Ma!

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MV-Learn

Machine Learning - python

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Lane-Extraction-from-Grass-Surfaces

A model to extract lanes from an image using semantic segmentation.

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road_connectivity

Improved Road Connectivity by Joint Learning of Orientation and Segmentation (CVPR2019)

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Enet

自己的Enet网络

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Road-Marking-Segmentation

A project for lane detection and road marking segmentation on embedded device for autonomous vehicles.

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FCN-Lane-Segementation-Autopilot

Lane Segementation with Fully Convolutional Network

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mobile-segmentation

Real-time semantic image segmentation on mobile devices

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android-segmentation-app

A basic Android application that runs semantic image segmentation using Tensorflow-Lite

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3D-SIS

[CVPR'19] 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans

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ultra_high_resolution_segmentation

code used for CVPR2019 oral "Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-High Resolution Images"

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TFSegmentation

RTSeg: Real-time Semantic Segmentation Comparative Study

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road-object-detection-for-bdd100k

Object detection model for BDD100K

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FeatherCNN

FeatherCNN is a high performance inference engine for convolutional neural networks.

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semantic_segmentation_unet

PyTorch implementation of semantic segmentation using UNET architecture on BDD100K dataset.

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TensorFlow-ENet

TensorFlow implementation of ENet

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test2

test

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