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/varunasthana92/YOLO_v3_implementation_Tensorflow2
https://github.com/vaseline555/Federated-Learning-in-PyTorch
https://github.com/vayuvegula/kerasModels
https://github.com/vectorch-ai/scalellm
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/venkateshdudipalli/Self-Driving-Car
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/vgangal101/resnet_models
https://github.com/victoria-lokteva/Performance-Monitoring-of-Object-Detection
Per-frame mAP Prediction for Continuous Performance Monitoring of Object Detection During Deployment
https://github.com/victorprad/InfiniTAM
InfiniTAM v3: A Framework for Large-Scale 3D Reconstruction with Loop Closure
https://github.com/vierachen/maskrcnn
https://github.com/vietanhdev/open-adas
https://github.com/vietdhoang/resnet-18
https://github.com/viethungluu/OCRetina
https://github.com/vietlinhtspt/NewFasterRCNN
https://github.com/vijaylaxmid/DeepLearning_3DPointClouds
https://github.com/vinay-EIP/EIP-session-5-assignment
https://github.com/vinbo/AirSim
AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles
https://github.com/vineeths96/Gradient-Compression
https://github.com/vineeths96/heterogeneous-systems
https://github.com/vinod377/STN-OCR
https://github.com/vinod377/STN-OCR-Tensorflow
https://github.com/vision-agh/pointpillars-backbone
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/vision-agh/pointpillars_backbone
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/viskjha/Wrong-way-vehicle-detection
https://github.com/vista-simulator/vista
https://github.com/vita-epfl/rock-pytorch
https://github.com/vita-epfl/s-attack
Are socially-aware trajectory prediction models really socially-aware?
https://github.com/vita-epfl/social-nce-crowdnav
Social NCE: Contrastive Learning of Socially-aware Motion Representations
https://github.com/vita-epfl/unitraj
Latent Variable Sequential Set Transformers For Joint Multi-Agent Motion Prediction
https://github.com/vlievin/Unet
https://github.com/vlislab22/360_Survey
Deep Learning for Omnidirectional Vision: A Survey and New Perspectives
https://github.com/vobecant/yolov3_WannaSeeU
https://github.com/vov-net/VoVNet-FCOS
https://github.com/vu-resilient-distributed-systems/lectures-fall-2019
Dynamic-Weighted Simplex Strategy for Learning Enabled Cyber Physical Systems
https://github.com/vuongtrannguyenkhoi/darknet
https://github.com/vxy10/p2-TrafficSigns
Traffic Sign Classification Using Deep Inception Based Convolutional Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.