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/safolab-wisc/fiubench
https://github.com/saikrishnadas/ResNet-Pytorch
https://github.com/saimj7/Social-Distancing-Detection-in-Real-Time
https://github.com/sair-lab/ni-slam
Correlation Flow: Robust Optical Flow Using Kernel Cross-Correlators
https://github.com/saitejavn/lgsvl-scenarios
https://github.com/saloni1998/Cifar-10
https://github.com/samadiamir/IE2E
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning
https://github.com/sammyamajumdar/ResNet50
https://github.com/samson6460/tf2_YOLO
https://github.com/samsonadmin/modified-alexeyab-darknet
https://github.com/sangHa0411/CIFAR100
https://github.com/sangHa0411/ImageNet
https://github.com/sangHa0411/ResNetCIFAR100
https://github.com/sanghyun-son/clustering-kernels
https://github.com/sanjeewk/ppnet
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/sanoyo/dl-model
https://github.com/saransh317/YOLOv3-Easy-Implementation
https://github.com/sarrrrry/maskrcnn-benchmark
https://github.com/sarthaksharma13/Earth-ain-t-Flat_IROS18
https://github.com/sarthaksharma13/IROS18
https://github.com/sarthaksharma13/RenderForCNN_KeypointGeneration
https://github.com/sarthaksharma13/RenderForCNN_annotation
https://github.com/sasha-kap/CV-to-Maps
Automatic Discovery and Geotagging of Objects from Street View Imagery
https://github.com/satishjasthi/Densenet_smplified
https://github.com/saumya0303/Attack_on_Image_Classification_Network
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
https://github.com/saumya0303/attack_image
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
https://github.com/saumya0303/image-classification-network-attack
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
https://github.com/sayakpaul/Adaptive-Gradient-Clipping
https://github.com/scope-lab-vu/deep-nn-car
Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions
https://github.com/scope-lab-vu/risk-aware-scene-generation-cps
https://github.com/sdret/a-solution-to-co-occurrence-bias-attributes-disentanglement-via-mutual-information-minimization-for
https://github.com/sdu2011/darknet_alexyab
https://github.com/seansoleyman/cifar10-resnet
https://github.com/seasonyc/densenet
https://github.com/sebsquire/Dogs-and-cats-image-classification-CNN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.