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/D-X-Y/ResNeXt-DenseNet
https://github.com/DIG-Beihang/OPIXray
https://github.com/DInesh1234-wq/tyredataset
https://github.com/DQSSSSS/VectorNet
VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation
https://github.com/DableUTeeF/keras-efficientnet
https://github.com/Daffan/ros_jackal
Benchmarking Reinforcement Learning Techniques for Autonomous Navigation
https://github.com/DaikiTanak/manifold_mixup
https://github.com/DaloroAT/first_break_picking
https://github.com/Daniel-Liu-c0deb0t/3D-Neural-Network-Adversarial-Attacks
https://github.com/DarkGeekMS/Pytorch-YOLOv3-Implementation
https://github.com/DataXujing/YOLOX-
https://github.com/DavianYang/yolo.ai
https://github.com/Davidnet/TFSegmentation
https://github.com/DeNA/PyTorch_YOLOv3
https://github.com/Deci-AI/super-gradients
https://github.com/DeepMark/deepmark
https://github.com/DeepTrial/Retina-VesselNet
https://github.com/Dekki-Aero/Self-Driving-Car
https://github.com/Demonhesusheng/darknet_v2
https://github.com/DengpanFu/LUPerson
https://github.com/DerrickXuNu/OpenCOOD
https://github.com/Derteanoo/PINet_quantization
Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
https://github.com/DetectionTeamUCAS/FCOS_GluonCV
https://github.com/DetectionTeamUCAS/FPN_Tensorflow
https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation
https://github.com/DevBruce/YOLOv3-TF2
https://github.com/Dikshuy/SMARTS-lite
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
https://github.com/Discover304/MMDetection3D-Eloss
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/Djmcflush/Funky-Movement
https://github.com/DogeZen/resnet2onnx2tensorrt
https://github.com/DominikFHG/TensorQuant
Sparsity in Deep Neural Networks - An Empirical Investigation with TensorQuant
https://github.com/DonCorle0ne/damage_detection
https://github.com/DrArryYao/RL-SC
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/DrMMZ/RetinaNet
https://github.com/Duckkkky/smarts
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.