Pinned Repositories
2018DSB
2018 Data Science Bowl 2nd Place Solution
3D-Deepbox
3D Bounding Box Estimation Using Deep Learning and Geometry (MultiBin)
3d-semantic-segmentation
This work is based on our paper Exploring Spatial Context for 3D Semantic Segmentation of Point Clouds, which is appeared at the IEEE International Conference on Computer Vision (ICCV) 2017, 3DRMS Workshop.
3dvie
test
acado
ACADO Toolkit is a software environment and algorithm collection for automatic control and dynamic optimization. It provides a general framework for using a great variety of algorithms for direct optimal control, including model predictive control, state and parameter estimation and robust optimization.
AdaBound
An optimizer that trains as fast as Adam and as good as SGD.
AI-Challenger-Caption-Competition
AI CHALLENGER 全球AI挑战赛 图像中文描述
deep-head-pose
:fire::fire: Deep Learning Head Pose Estimation using PyTorch.
iceberg_starter
gluon starter for https://www.kaggle.com/c/statoil-iceberg-classifier-challenge
iNaturalist
MXNet finetune baseline (res152) for challenger.ai/competition/scene
wzhang1's Repositories
wzhang1/attention-cnn
Source code for "On the Relationship between Self-Attention and Convolutional Layers"
wzhang1/CameraRadarFusionNet
wzhang1/carla
Open-source simulator for autonomous driving research.
wzhang1/Codes-for-Lane-Detection
Tensorflow implementation of "Spatial As Deep: Spatial CNN for Traffic Scene Understanding" (AAAI 2018)
wzhang1/DeLS-3D
The code for DeLS-3D of CVPR 2018
wzhang1/EfficientNet-PyTorch
A PyTorch implementation of EfficientNet
wzhang1/frankmocap
A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
wzhang1/HRNet-Image-Classification
Train the HRNet model on ImageNet
wzhang1/HRNet-MaskRCNN-Benchmark
Object detection with multi-level representations generated from deep high-resolution representation learning (HRNetV2h).
wzhang1/HRNet-Semantic-Segmentation
High-resolution representation learning (HRNets) for Semantic Segmentation
wzhang1/imet-6th-soltuion
Code for iMet Collection 2019 - FGVC6
wzhang1/InceptionTime
InceptionTime: Finding AlexNet for Time Series Classification
wzhang1/kaggle-imaterialist
The First Place Solution of iMaterialist (Fashion) 2019 at FGVC6
wzhang1/KFNet
KFNet: Learning Temporal Camera Relocalization using Kalman Filtering (CVPR 2020 Oral)
wzhang1/Landmark2019-1st-and-3rd-Place-Solution
The 1st Place Solution of the Google Landmark 2019 Retrieval Challenge and the 3rd Place Solution of the Recognition Challenge.
wzhang1/ld-lsi
lane/freespace detector built for UC3M LSI
wzhang1/libxcam
libXCam is a project for extended camera(not limited in camera) features and focus on image quality improvement and video analysis. There are lots features supported in image pre-processing, image post-processing and smart analysis. This library makes GPU/CPU/ISP working together to improve image quality. OpenCL is used to improve performance in different platforms.
wzhang1/lingvo
Lingvo
wzhang1/mAP
mean Average Precision - This code evaluates the performance of your neural net for object recognition.
wzhang1/open-simulation-interface
A generic interface for the environmental perception of automated driving functions in virtual scenarios.
wzhang1/openfortivpn
Client for PPP+SSL VPN tunnel services
wzhang1/OpenPCDet
OpenPCDet Toolbox for LiDAR-based 3D Object Detection.
wzhang1/PINet_new
wzhang1/SCNN_Pytorch
Pytorch implementation of "Spatial As Deep: Spatial CNN for Traffic Scene Understanding"
wzhang1/ShuffleNet-Series
wzhang1/simpledet
A Simple and Versatile Framework for Object Detection and Instance Recognition
wzhang1/stripe-mock
stripe-mock is a mock HTTP server that responds like the real Stripe API. It can be used instead of Stripe's testmode to make test suites integrating with Stripe faster and less brittle.
wzhang1/Templates-for-Competitive-Programming
wzhang1/timeseriesAI
Practical Deep Learning for Time Series / Sequential Data using fastai/ Pytorch
wzhang1/transformer
Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.