Papers with Code 아카이브
Autonomous Vehicles
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/yasutomo57jp/openpifpaf_ros
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
https://github.com/yaxi333/PointPainting-Modified
https://github.com/ycao5602/SAL
https://github.com/ycchiusieve/yolo3
https://github.com/yet124/yolov3-pytorch
https://github.com/yfeng002/impvaeood
https://github.com/yfnn/fused-model
https://github.com/yfnn/person_subclass
https://github.com/yhbcode000/MMDetection3D-Eloss
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/yhenon/pytorch-retinanet
https://github.com/yibeiou/Advanced_Business_Intelligence
https://github.com/yichigo/Chest-X-Ray
https://github.com/yijiaceline/Final-Project-Group4
https://github.com/yiningzeng/darknet-fork-from-AlexeyAB
https://github.com/yoshitomo-matsubara/bottlefit-split_computing
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/yoshitomo-matsubara/head-network-distillation
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/yoshitomo-matsubara/hnd-ghnd-object-detectors
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/yoshitomo-matsubara/sc2-benchmark
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/yoshitomo-matsubara/supervised-compression
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/yoshitomo-matsubara/torchdistill
https://github.com/youngsjjn/MemSeg
Memory-based Semantic Segmentation for Off-road Unstructured Natural Environments
https://github.com/youssef-kishk/ROS-Based-Robot-Object-Detection-Recognition
https://github.com/yqyao/FCOS_PLUS
https://github.com/yrouphail/yolo-independent
https://github.com/yrouphail/yolov3
https://github.com/yuant95/SMARTS_VCR
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
https://github.com/yudhisteer/Face-Recognition-with-Masks
https://github.com/yudie433/darknet
https://github.com/yuedongli1/yolov3_mindspore
https://github.com/yueruchen/sppnet-pytorch
Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
https://github.com/yukaryote/darknet
https://github.com/yukitsuji/3D_CNN_tensorflow
3D Fully Convolutional Network for Vehicle Detection in Point Cloud
https://github.com/yuliani29/yolotraining
https://github.com/yungecui/link3d
BoW3D: Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM
https://github.com/yushuz/me599_yolo
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.