Papers with Code 아카이브

Autonomous Vehicles

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 15
평가표: 1
보존된 평가 기록: 2

Autonomous vehicles is the task of making a vehicle that can guide itself without human conduction.

Many of the state-of-the-art results can be found at more general task pages such as 3D Object Detection and Semantic Segmentation.

<span style="color:grey; opacity: 0.6">( Image credit: GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/jm12138/car-behavioral-cloning-paddle

End to End Learning for Self-Driving Cars

https://github.com/jmesich/chestxray

Densely Connected Convolutional Networks

https://github.com/jmfacil/single-view-place-recognition

Single-View Place Recognition under Seasonal Changes

https://github.com/jndeng/DACSDC-DeepZ

Feature Pyramid Networks for Object Detection

https://github.com/joanfmendo/prop-dronet

A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

https://github.com/joeynavarro/facial_expression_classification

Deep Residual Learning for Image Recognition

https://github.com/joheras/darknet-colab

Focal Loss for Dense Object Detection

https://github.com/johngear/eecs504

Deep Residual Learning for Image Recognition

https://github.com/johnnylu305/resnet-50-101-152

Deep Residual Learning for Image Recognition

https://github.com/johnsebin97/darknet

YOLOv3: An Incremental Improvement

https://github.com/johntd54/stanford_car

Densely Connected Convolutional Networks

https://github.com/johnwlambert/argoverse_cbgs_kf_tracker

Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

https://github.com/jolibrain/caffe

Focal Loss for Dense Object Detection

https://github.com/jonnor/datascience-master

Densely Connected Convolutional Networks

https://github.com/jonvthvn90/Project

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/jostl/masters-thesis

Learning by Cheating

https://github.com/jsiloto/adaptive-cod

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/jtcass01/SIMON

Deep Residual Learning for Image Recognition

https://github.com/juergenlandauer/Maya-Challenge

Focal Loss for Dense Object Detection

https://github.com/juglab/EmbedSeg

Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth

https://github.com/julik43/IIMAS-USCS

Deep Residual Learning for Image Recognition

https://github.com/jung-jun-uk/mixface

Deep Residual Learning for Image Recognition

https://github.com/justinkay/repulsion-loss-detectron2

Repulsion Loss: Detecting Pedestrians in a Crowd

https://github.com/jvoynow/ConvNet-Architectures

Deep Residual Learning for Image Recognition

https://github.com/jwchoi384/Gaussian_YOLOv3

Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving

https://github.com/k-miran/hear

Deep Residual Learning for Image Recognition

https://github.com/k5iogura/darknetAB

YOLOv3: An Incremental Improvement

https://github.com/kadirnar/yolov6-pip

YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

https://github.com/kadirnar/yolov7-pip

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

https://github.com/kaist-vclab/sphere-stereo

The Double Sphere Camera Model

https://github.com/kakaoenterprise/Learning-Debiased-Disentangled

RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening

https://github.com/kalebbennaveed/Trajectory-Planning-for-Autonomous-Vehicles-Using-HRL

Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning

https://github.com/kalelpark/ral

Focal Loss for Dense Object Detection

https://github.com/kalviny/IMTA

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/kalyo-zjl/APD

Attribute-aware Pedestrian Detection in a Crowd

https://github.com/kanji95/carla-nav-tool

Ground then Navigate: Language-guided Navigation in Dynamic Scenes

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.