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/Hanson0910/RetinaTrack

RetinaTrack: Online Single Stage Joint Detection and Tracking

https://github.com/Harlequln/C1M18X-Behavioural_Cloning

End to End Learning for Self-Driving Cars

https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning

Deep Residual Learning for Image Recognition

https://github.com/HarshaVardhanVanama/Autopilot-Steering-Control

End to End Learning for Self-Driving Cars

https://github.com/HarshaVardhanVanama/Autopilot-Steering-Wheel-Simulation

End to End Learning for Self-Driving Cars

https://github.com/Hashir44/muraxray

Densely Connected Convolutional Networks

https://github.com/HengWeiBin/CenterFusionDetect3D

CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection

https://github.com/Hernandope/keras_resnet_FPN_cifar10

Feature Pyramid Networks for Object Detection

https://github.com/Hiwyl/yolov3-attentation

YOLOv3: An Incremental Improvement

https://github.com/HongSic/DarknetAI

Focal Loss for Dense Object Detection

https://github.com/Hossein-Bodaghi/SI_attributes

https://github.com/Hp990/Self-driving-car

End to End Learning for Self-Driving Cars

https://github.com/Hrishkesh/YoloInvoiceExtraction

YOLOv3: An Incremental Improvement

https://github.com/Huangdebo/YOLOv4-MultiTask

Ultra Fast Structure-aware Deep Lane Detection

https://github.com/HussainGhazaly/PINet-master

Key Points Estimation and Point Instance Segmentation Approach for Lane Detection

https://github.com/IBM/Autozoom-Attack

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

https://github.com/IBM/NeuronAlignment

Deep Residual Learning for Image Recognition

https://github.com/IBM/ZOO-Attack

ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

https://github.com/IMAC-projects/mesh-deformation

Accelerating 3D Deep Learning with PyTorch3D

https://github.com/IMvision12/keras-vision-models

Deep Residual Learning for Image Recognition

https://github.com/IPLI/IPLI_Server

Focal Loss for Dense Object Detection

https://github.com/IamWangYunKai/DG-TrajGen

Domain Generalization for Vision-based Driving Trajectory Generation

https://github.com/Iamal1/maskrcnn-benchmark

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/IgorMunizS/mobileDet

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/IlyaOvodov/AngelinaReader

Focal Loss for Dense Object Detection

https://github.com/Information-Fusion-Lab-Umass/Salient-Pedestrian-Detection

Pedestrian Detection in Thermal Images using Saliency Maps

https://github.com/IntelLabs/AVUC

Loss-Calibrated Approximate Inference in Bayesian Neural Networks

https://github.com/InterDigitalInc/CompressAI-Vision/blob/main/compressai_vision/codecs/sic_sfu2022.py

Scalable Image Coding for Humans and Machines

https://github.com/Iqbal1282/yolov3_incremental_distillation_loss

YOLOv3: An Incremental Improvement

https://github.com/IrohXu/lanenet-lane-detection-pytorch

Towards End-to-End Lane Detection: an Instance Segmentation Approach

https://github.com/IvanVassi/LaneNet

Towards End-to-End Lane Detection: an Instance Segmentation Approach

https://github.com/JCamERG/YOLOPersonal

YOLOv3: An Incremental Improvement

https://github.com/JContro/pytorch_projects

Deep Residual Learning for Image Recognition

https://github.com/JEONGHA-SHIN/YOGO_Server_v2

YOLOv3: An Incremental Improvement

https://github.com/JHLee0513/Salt_detection_challenge

Deep Residual Learning for Image Recognition

https://github.com/JHUVisionLab/multi-modal-regression

A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images

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