Papers with Code 아카이브

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/shreyaspj20/Autonomous-Driving-Car-simulation-using-Pytorch

End to End Learning for Self-Driving Cars

https://github.com/shtnkgm/VisionFrameworkSample

Deep Residual Learning for Image Recognition

https://github.com/shuchun1997/Dynamic_slam-orbslam-

DynaSLAM: Tracking, Mapping and Inpainting in Dynamic Scenes

https://github.com/shun601/4th-tellus-satellite-challenge

Feature Pyramid Networks for Object Detection

https://github.com/shunchan0677/deepware

End to End Learning for Self-Driving Cars

https://github.com/shyhyawJou/Binarized-Neural-Network

Architecturing Binarized Neural Networks for Traffic Sign Recognition

https://github.com/siat-invs/carlaflcav

Edge Federated Learning Via Unit-Modulus Over-The-Air Computation

https://github.com/sidSingla/RegNetx-YOLOv3

YOLOv3: An Incremental Improvement

https://github.com/siddhanthaldar/PyTorch_Object_Detection

CFENet: An Accurate and Efficient Single-Shot Object Detector for Autonomous Driving

https://github.com/sidify/resnet_focal_loss

Deep Residual Learning for Image Recognition

https://github.com/simonlevine/11785-project

Focal Loss for Dense Object Detection

https://github.com/simonlevine/clinical-longformer

Focal Loss for Dense Object Detection

https://github.com/simsal0r/mixture-of-decision-trees

MoËT: Mixture of Expert Trees and its Application to Verifiable Reinforcement Learning

https://github.com/sisl/InterpretableValidation.jl

Interpretable Safety Validation for Autonomous Vehicles

https://github.com/sithu31296/semantic-segmentation

Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes

https://github.com/sjchoi86/choicenet

Task Agnostic Robust Learning on Corrupt Outputs by Correlation-Guided Mixture Density Networks

https://github.com/skbadhsm/darknet2

Focal Loss for Dense Object Detection

https://github.com/slightech/MYNT-EYE-VIORB-Sample

Visual-Inertial Monocular SLAM with Map Reuse

https://github.com/smc-x/ms-resnetv2

Deep Residual Learning for Image Recognition

https://github.com/smlbansal/deepreach

DeepReach: A Deep Learning Approach to High-Dimensional Reachability

https://github.com/snbhanja/SelfDrivingCarND_Term1_Project4_Behavioral_cloning

End to End Learning for Self-Driving Cars

https://github.com/snehitvaddi/SelfDriving-Car_Deep-Learning

End to End Learning for Self-Driving Cars

https://github.com/sobhan-moosavi/characterizingdrivingstyleswithdeeplearning

Characterizing Driving Styles with Deep Learning

https://github.com/soccergame/darknet

Focal Loss for Dense Object Detection

https://github.com/soeaver/py-RFCN-priv

Feature Pyramid Networks for Object Detection

https://github.com/solapark/da_yolo

Focal Loss for Dense Object Detection

https://github.com/solapark/darknet_interpark

Focal Loss for Dense Object Detection

https://github.com/solapark/darknet_partdet

Focal Loss for Dense Object Detection

https://github.com/solapark/darknet_partdet_tmp

Focal Loss for Dense Object Detection

https://github.com/sonnguyen64/horus-tf-faster-rcnn

Feature Pyramid Networks for Object Detection

https://github.com/souravverma94/pre_trained_imgclassifier_dogBreeds

Deep Residual Learning for Image Recognition

https://github.com/sovit-123/traffic-light-detection-using-yolov3

A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection

https://github.com/sparvangada/capstone_project

Densely Connected Convolutional Networks

https://github.com/speckean/upar_challenge

UPAR: Unified Pedestrian Attribute Recognition and Person Retrieval

https://github.com/spirka3/master-cnn

Predicting Driver Attention in Critical Situations

https://github.com/srihari-humbarwadi/retinanet-tensorflow2.x

Focal Loss for Dense Object Detection

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