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Autonomous Vehicles

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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/Duplums/bhb10k-dl-benchmark

Deep Residual Learning for Image Recognition

https://github.com/EMI-Group/FaPN

Feature Pyramid Networks for Object Detection

https://github.com/EMsnap/RobotSorting

Focal Loss for Dense Object Detection

https://github.com/ENSTA-U2IS/MUAD-Dataset

MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasks

https://github.com/EbadSyed/spadRGBD

Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image

https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation

Deep Residual Learning for Image Recognition

https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation

Deep Residual Learning for Image Recognition

https://github.com/Eisbaer8/DeepGTAV

Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?

https://github.com/ElSchuer/e2e_lane_keeping

End to End Learning for Self-Driving Cars

https://github.com/ElementAI/baal

DropConnect Is Effective in Modeling Uncertainty of Bayesian Deep Networks

https://github.com/Elvictor01/Plant-Pathology-2020---FGVC7-Identify-the-category-of-foliar-diseases-in-apple-trees

Densely Connected Convolutional Networks

https://github.com/EmGarr/kerod

Feature Pyramid Networks for Object Detection

https://github.com/EmergentSystemLabStudent/SpCoSLAM

Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping

https://github.com/EmergentSystemLabStudent/SpCoSLAM_Lets

Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping

https://github.com/EricAlcaide/MiniFold

Deep Residual Learning for Image Recognition

https://github.com/EricWiener/point-cloud-de-noising

CNN-based Lidar Point Cloud De-Noising in Adverse Weather

https://github.com/EstherBear/implementation-of-pruning-filters

Deep Residual Learning for Image Recognition

https://github.com/FL77N/RetinaNet-Based-on-PPdet

Focal Loss for Dense Object Detection

https://github.com/FabrizioPuzzo/CarND-Behavioral-Cloning-P4

End to End Learning for Self-Driving Cars

https://github.com/FabrizioPuzzo/End-to-End-Learning

End to End Learning for Self-Driving Cars

https://github.com/FarzamIHPC/YoloV3_mask

YOLOv3: An Incremental Improvement

https://github.com/FeiyangJin/airsim-ros-backup

AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

https://github.com/FloHofstetter/wcid-keras

Where can I drive? A System Approach: Deep Ego Corridor Estimation for Robust Automated Driving

https://github.com/FlorinAndrei/TensorAim

YOLOv3: An Incremental Improvement

https://github.com/FluteXu/ms-project

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/FlyingAnt2018/darknet-modify

YOLOv3: An Incremental Improvement

https://github.com/Fmohammadsofi/software_defect_predicition_using_LLMs

Multimodal Learning for Just-In-Time Software Defect Prediction in Autonomous Driving Systems

https://github.com/FrancescoSaverioZuppichini/ResNet

Deep Residual Learning for Image Recognition

https://github.com/FreeformRobotics/semantic-histogram-based-global-localization

Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment

https://github.com/Fw9wef/DetectorGAN

Generative Modeling for Small-Data Object Detection

https://github.com/GK4444/Self-Driving-Car

End to End Learning for Self-Driving Cars

https://github.com/GKalliatakis/Keras-VGG16-places365

Deep Residual Learning for Image Recognition

https://github.com/GSNCodes/Breast_Cancer_Detection_CNN

Densely Connected Convolutional Networks

https://github.com/GT-RIPL/L2C

Learning to Cluster for Proposal-Free Instance Segmentation

https://github.com/GT-RIPL/robo-vln

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

https://github.com/GXYM/Focal-loss

Focal Loss for Dense Object Detection

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