Papers with Code 아카이브
Autonomous Vehicles
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/RaghavHub/rekuten_hack
https://github.com/Rahmanzia3/darknet
https://github.com/Rahmanzia3/darknet_original
https://github.com/Rahmanzia3/yolo
https://github.com/RajdeepBiswas/Manufacturing-Quality-Inspection
https://github.com/RajuGudhe/Cancer_Tissue_Detection
https://github.com/Rakshit-Shetty/Resnet-Implementation
https://github.com/RameshBattu/Convolutional-Neural-Network-on-CIFAR-10-DataSet
https://github.com/RashadGarayev/PersonDetection
https://github.com/Raychou0830/Carplate
https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
https://github.com/RemyMess/MMVRC_ICCV_2021_Skeleton_based_Action_Recognition
UAV-Human: A Large Benchmark for Human Behavior Understanding with Unmanned Aerial Vehicles
https://github.com/RentadroneCL/Photovoltaic_Fault_Detector
https://github.com/RentadroneCL/model-definition
https://github.com/RenzKa/simlingo
https://github.com/Ricardozzf/sdsrcnn
Illuminating Pedestrians via Simultaneous Detection & Segmentation
https://github.com/RichardMathewsII/YOLBO
https://github.com/RichardMinsooGo-ML/Pytorch-Complex-Yolo-Yolov3
https://github.com/RichardMinsooGo-ML/Pytorch-Complex-Yolo-Yolov4
https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov3
https://github.com/RichardMinsooGo-ML/Pytorch-Yolo-3d-Yolov4
https://github.com/Ritik-Sharma38/YoloV3_training_Hands
https://github.com/RobotMobile/cv-deep-learning-paper-review
https://github.com/Robotmurlock/TNT-VectorNet-and-HOME-Trajectory-Forecasting
https://github.com/Rohed/ml-1
https://github.com/RoyMachineLearning/Deep-Learning-Self-Driving-Simulator
https://github.com/RuoyuChen10/objectdetection-saliency-maps
https://github.com/RyanXingQL/RBQE
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/SAIGANESH-Collab/DARKNET
https://github.com/SAIGANESH-Collab/YOLO-AQUA
https://github.com/SANKHA1/Vehicle-Detection
https://github.com/SGPHD/E-Scooter_Rider_Detection_Dataset
https://github.com/SHITIANYU-hue/Efficient-motion-planning
https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection
https://github.com/SSN15/Behavioral-Cloning--Implementaion-of-Autonomous-car-using-deep-learning
ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.