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/ajaystar8/PDRUNet-PyTorch
https://github.com/ajithvallabai/Pedestrian_Attribute_Recognition
A Richly Annotated Dataset for Pedestrian Attribute Recognition
https://github.com/ajithvallabai/getsetgo_keras-beginner
https://github.com/ajoshi944/Segmentation-severstal-steel
Squeeze-SegNet: A new fast Deep Convolutional Neural Network for Semantic Segmentation
https://github.com/akamaster/pytorch_resnet_cifar10
https://github.com/akar5h/Image-Recognition-with-CNN-and-Transfer-Learning-ResNet101
https://github.com/akhadangi/EM-net
https://github.com/akommini/Spoken-Numeric-Digit-detection
https://github.com/akshaybahadur21/Autopilot
https://github.com/aksoyeren/salsanet
SalsaNet: Fast Road and Vehicle Segmentation in LiDAR Point Clouds for Autonomous Driving
https://github.com/alalagong/LEDNet
https://github.com/alannguyencs/maskrcnn
https://github.com/albarji/neurowriter
https://github.com/albertsokol/yolov3-tf2
https://github.com/alecwangcq/KFAC-Pytorch
https://github.com/alex-f1tor/Image-Caption
https://github.com/alexa/wqa-cascade-transformers
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/alexdominguez09/pedestrian_direction
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/alexismailov2/darknet
https://github.com/alexsax/midlevel-reps
https://github.com/alexwdong/IncubatorCVProject
https://github.com/aliansgp/RL-SAC-CARLA
Autonomous Driving using Residual Sensor Fusion and Deep Reinforcement Learning
https://github.com/alibaba/EasyCV
https://github.com/alicranck/instance-seg
Semantic Instance Segmentation with a Discriminative Loss Function
https://github.com/alililia/ascend_FaceAttribute
https://github.com/alililia/ascend_SE-Net
https://github.com/alililia/ms_extend/tree/main/gpu_resnet
https://github.com/alin256/multi-mode-prediction-with-mtp-loss
Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks
https://github.com/alisha17/benchmarks
https://github.com/aljosaosep/kitti-track-collection
Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video
https://github.com/allenai/robothor-challenge
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
https://github.com/allenai/robustnav
PyRobot: An Open-source Robotics Framework for Research and Benchmarking
https://github.com/alrojo/lasagne_residual_network
https://github.com/alt113/flow-triangle-scenario
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
https://github.com/alvarobartt/serving-pytorch-models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.