Pinned Repositories
-Intelligent-application-of-traffic-scene-based-on-computer-vision-ODT
基于计算机视觉的交通场景智能应用(目标检测与目标追踪)
123
123
3D-Point-Cloud-Analytics
Portfolio for 3D Point Cloud Processing from www.shenlanxueyuan.com China
AIC21-MTMC
🏆The 1st place solution of track3 (City-Scale Multi-Camera Vehicle Tracking) in the NVIDIA AI City Challenge at CVPR 2021 Workshop.
AnyNet
(ICRA) Anytime Stereo Image Depth Estimation on Mobile Devices
AyDS
BaiduBodyAnalyzeDemo
百度人体分析Demo:人体关键点、人体属性、手势识别、人像分割、人流量统计、驾驶行为分析(邀测)、人流量统计动态版(邀测)
car_classification_tracking_and_more
Cascade-RCNN-Tracking
Cascade-RCNN+DeepSort MOTDT Trackor++
Film-Recommendation-System
使用Tensoflow构建电影推荐系统
gitqinxinyu's Repositories
gitqinxinyu/PyRetri
Open source deep learning based unsupervised image retrieval toolbox built on PyTorch🔥
gitqinxinyu/Film-Recommendation-System
使用Tensoflow构建电影推荐系统
gitqinxinyu/SDCN
Structural Deep Clustering Network
gitqinxinyu/ML-NLP
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现,也是作为一个算法工程师必会的理论基础知识。
gitqinxinyu/Rolling-bearing-fault-diagnosis-use-wavelet-and-CNN
对轴承数据进行小波包变换提取其中的能量谱特征共256维。将其转化为16*16的矩阵输入进CNN进行故障诊断分类
gitqinxinyu/frustum_pointnets_pytorch
A pytorch version of frustum-pointnets
gitqinxinyu/PointCloudSegmentation
Experiments on point cloud segmentation.
gitqinxinyu/PointAugment
Code for PointAugment: an Auto-Augmentation Framework for Point Cloud Classification, CVPR 2020 (Oral)
gitqinxinyu/FFA-Net
FFA-Net: Feature Fusion Attention Network for Single Image Dehazing
gitqinxinyu/cctv-prototype-deepsort
A small 2-day prototype of cctv camera tracking cars and humans
gitqinxinyu/SOT_Code_Comment
基于孪生网络(SiamNet)的单目标跟踪算法
gitqinxinyu/Pointnet_Pointnet2_pytorch
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
gitqinxinyu/AyDS
gitqinxinyu/product-quantization
🙃Implementation of vector quantization algorithms, codes for Norm-Explicit Quantization: Improving Vector Quantization for Maximum Inner Product Search..
gitqinxinyu/Wind_predict_seq2seq
基于seq2seq模型的风功率预测
gitqinxinyu/pqkmeans
Fast and memory-efficient clustering
gitqinxinyu/SeetaFace2AndroidDemo
SeetaFace2 的android示例
gitqinxinyu/FaceRecognition
gitqinxinyu/nanopq
Pure python implementation of product quantization for nearest neighbor search
gitqinxinyu/python-wavelets
学习小波
gitqinxinyu/gcn_clustering
Code for CVPR'19 paper Linkage-based Face Clustering via GCN
gitqinxinyu/Predictive-Maintenance-using-LSTM
Example of Multiple Multivariate Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras.
gitqinxinyu/Pytorch-LSTM-Stock-Price-Predict
LSTM 实现的股票最高价预测
gitqinxinyu/git
git文档
gitqinxinyu/BaiduBodyAnalyzeDemo
百度人体分析Demo:人体关键点、人体属性、手势识别、人像分割、人流量统计、驾驶行为分析(邀测)、人流量统计动态版(邀测)
gitqinxinyu/pytorch_seq2seq_toy
基于pytorch的seq2seq模型
gitqinxinyu/123
123
gitqinxinyu/Deep-Learning-PointNet-Project
Classification and Segmentation of the MNIST dataset given as a point set input. Classification: the program classifies hand written digits, given as a sample of 100 points in a 2 dimensional field. the architecture is based on a Stanford article of a PointNet which is especially efficient for 3D image classification. the PointNet classification accuracy is 92.86% Segmentation: this is an extension to the classification net which can later define segments within the pointset. the program receives an input of a handwritten digit, given as a sample of 200 points in a 2 dimensional field, where 100 of the points are a sample of the digit itself, and the rest of the points are "background" points which are not part of the digit. the program classifies each point into one of the 2 segments and returns if it is part of the digit or part of the background. the PointNet segmentation accuracy is 97.65%