Autonomous Vehicles
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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/tallysprado/myDarknet
https://github.com/tanimutomo/ganonymizer
https://github.com/tanjeffreyz/deep-residual-learning
https://github.com/tanjeffreyz/yolo-v1
https://github.com/tanmayj000/darknet
https://github.com/tantara/PeerNets-pytorch
PeerNets: Exploiting Peer Wisdom Against Adversarial Attacks
https://github.com/taylor-914/DenseNet
https://github.com/tcheung99/ResNet_MiniProject
https://github.com/tcwangshiqi-columbia/Neurify
Formal Security Analysis of Neural Networks using Symbolic Intervals
https://github.com/tcwangshiqi-columbia/ReluVal
Formal Security Analysis of Neural Networks using Symbolic Intervals
https://github.com/tdurand/darknet-withtiling
https://github.com/teemoeric/projet
https://github.com/tejas-gokhale/AGAT
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
https://github.com/tensorflow/lingvo/blob/014007fbed690f6a10b51b630d7a4d0dd6cfd5df/lingvo/tasks/car/params/waymo_deepfusion.py
Learning an Uncertainty-Aware Object Detector for Autonomous Driving
https://github.com/tensorflow/models
https://github.com/tensorflow/models/tree/master/research/deeplab
https://github.com/tensorflow/models/tree/master/research/slim
https://github.com/tensorpack/tensorpack
https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN
https://github.com/tensorpack/tensorpack/tree/master/examples/ResNet
https://github.com/testingautomated-usi/surprise-adequacy
Guiding Deep Learning System Testing using Surprise Adequacy
https://github.com/texasinstruments/edgeai-yolox
https://github.com/thaipduong/sbkm
Autonomous Navigation in Unknown Environments using Sparse Kernel-based Occupancy Mapping
https://github.com/thanhtran98/yolov3_jetbot
https://github.com/thanhtrung98/Pose_estimation
https://github.com/thanhtrung98/human_pose_estimation
https://github.com/the-super-toys/glimpse-models
https://github.com/theAIGuysCode/yolov3_deepsort
https://github.com/thecryboy/DECONV
https://github.com/thecryboy/yolov3_ultralytics_deconv
https://github.com/theia-4869/semantic-poss-api
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
https://github.com/theneuralbeing/behavioral-cloning
https://github.com/theshuv/Autonomous-vehicle-steering-angle-prediction
https://github.com/theycallmepeter/pytorch3d_PBR
https://github.com/thinhhoang95/helipad-yolo
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.