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ruguowoshiyu's Projects

3dod_thesis icon 3dod_thesis

3D Object Detection for Autonomous Driving in PyTorch, trained on the KITTI dataset.

awesome-lidar-labeling icon awesome-lidar-labeling

A collection of annotation tools for LiDAR Point Cloud. There are 3D Object Detection directions, and Semantic Segmentation.

business-information-picture-ocr-model icon business-information-picture-ocr-model

本模型是基于Python3编写的工商信息图片识别文字模型。主要步骤包括去噪、分离水印、反色、二值化增强等，支持批量图片处理。

cia-ssd-ai-trt icon cia-ssd-ai-trt

CIA-SSD ALL IN TensorRT,NMS not implemented in TensorRT,implemented in c++

cnn_seg_lidar icon cnn_seg_lidar

Real time 3D semantic segmentation for Lidar, Ros based project

crb-active-3ddet icon crb-active-3ddet

The official PyTorch implementation of "Exploring Active 3D Object Detection from a Generalization Perspective" (ICLR Spotlight 2023).

crnn_ocr_lite icon crnn_ocr_lite

CRNN for OCR with depthwise separable convolutions and a simple spatial transformer module [keras+tf]

deye icon deye

Keep an Eye on Defects Inspection.

dsvt-ai-trt icon dsvt-ai-trt

DSVT: Dynamic Sparse Voxel Transformer with Rotated Sets(CVPR2023),vaymo vehicle 3D Object Detection(top2), waymo cyclist 3D Object Detection(top1),waymo pedestrian 3D Object Detection(top1)

e2e-mlt icon e2e-mlt

E2E-MLT - an Unconstrained End-to-End Method for Multi-Language Scene Text

efficient-segmentation-networks icon efficient-segmentation-networks

Lightweight models for real-time semantic segmentationon PyTorch (include SQNet, LinkNet, SegNet, UNet, ENet, ERFNet, EDANet, ESPNet, ESPNetv2, LEDNet, ESNet, FSSNet, CGNet, DABNet, Fast-SCNN, ContextNet, FPENet, etc.)

gitignore icon gitignore

A collection of useful .gitignore templates

hyperfas icon hyperfas

静默活体检测 silent face anti-spoofing attack detection

hyperlpr icon hyperlpr

基于深度学习高性能中文车牌识别 High Performance Chinese License Plate Recognition Framework.

hypervid icon hypervid

开源移动端车型识别 Mobile Plateform Vehicle Identification Model

iclr2017mcnet icon iclr2017mcnet

Tensorflow implementation of the ICLR 2017 paper: Decomposing Motion and Content for Natural Video Sequence Prediction

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