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/rafaelmata357/Flower-image-classifier

Densely Connected Convolutional Networks

https://github.com/raghakot/keras-resnet

Deep Residual Learning for Image Recognition

https://github.com/rahowa/catboost_focal_loss

Focal Loss for Dense Object Detection

https://github.com/rahul-t-p/ASVspoof-2019

Deep Residual Learning for Image Recognition

https://github.com/rahul263-stack/Self-Driving-Car

End to End Learning for Self-Driving Cars

https://github.com/rahulchaurasiya1/cautious-waddle

Deep Residual Learning for Image Recognition

https://github.com/rainofmine/Face_Attention_Network

Focal Loss for Dense Object Detection

https://github.com/rajathik/ObjectDetection

RUHSNet: 3D Object Detection Using Lidar Data in Real Time

https://github.com/rajkumargithub/densenet.mura

Densely Connected Convolutional Networks

https://github.com/rajneeshaggarwal/google-efficientnet

Deep Residual Learning for Image Recognition

https://github.com/ran-weii/cleanil

When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning

https://github.com/raphael-cohen/capsnet_for_Faster-RCNN

Feature Pyramid Networks for Object Detection

https://github.com/rasmith/vllm

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/raspstephan/sugar-flower-fish-or-gravel

Focal Loss for Dense Object Detection

https://github.com/raulmur/ORB_SLAM

ORB-SLAM: a Versatile and Accurate Monocular SLAM System

https://github.com/raulmur/ORB_SLAM2

ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras

https://github.com/ravenagcaoili/yolov3

YOLOv3: An Incremental Improvement

https://github.com/ravi02512/efficientdet-keras

YOLOv3: An Incremental Improvement

https://github.com/ray-project/ray/tree/master/rllib

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

https://github.com/realcrane/BASAR-Black-box-Attack-on-Skeletal-Action-Recognition

Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack

https://github.com/realcrane/Defending-Black-box-Skeleton-based-Human-Activity-Classifiers

Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack

https://github.com/realcrane/robustactionrecogniser

Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack

https://github.com/realmichaelye/Skin-Lesions-Classifier-ResNet50

Deep Residual Learning for Image Recognition

https://github.com/rebeccagio/object_detection

Focal Loss for Dense Object Detection

https://github.com/redwankarimsony/darknet-yolov4-working

YOLOv3: An Incremental Improvement

https://github.com/rehamessameltagoury/Urban-Self-Driving-Car

End to End Learning for Self-Driving Cars

https://github.com/remic-othr/openmibood

Scaling Out-of-Distribution Detection for Real-World Settings

https://github.com/remusionita/flow_rl

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/ren365/RetrofitAI-matlab

Bayesian Learning-Based Adaptive Control for Safety Critical Systems

https://github.com/renmengye/rec-attend-public

End-to-End Instance Segmentation with Recurrent Attention

https://github.com/researchmm/SiamDW

Deep Residual Learning for Image Recognition

https://github.com/resuldagdanov/DeFIX

DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving

https://github.com/resuldagdanov/self-improving-RL

Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms

https://github.com/retis-ai/patchattacktool

On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving

https://github.com/richardehughes/chnflow

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/ricky40403/Fcos_seg

FCOS: Fully Convolutional One-Stage Object Detection

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