Papers with Code 아카이브
Autonomous Vehicles
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/atlas604/behavioral-cloning
https://github.com/aurelien-peden/Deep-Learning-paper-implementations
https://github.com/autoliuweijie/FastBERT
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/avocardio/resnet_vs_convnext
https://github.com/axmoyal/Steganalysis-with-CNN
https://github.com/ayanc/fdscs
Fast Deep Stereo with 2D Convolutional Processing of Cost Signatures
https://github.com/ayusefi/Localization-Papers
Intention-Net: Integrating Planning and Deep Learning for Goal-Directed Autonomous Navigation
https://github.com/ayush111111/cone_keypoint_regression
https://github.com/ayushpalak/darknet_cokebottles
https://github.com/ayushpalak/darknet_multi_cokebottles
https://github.com/ayushpatidar/PR1-DenseNet
https://github.com/azima-fariha/DeepLearning_DenselyConnectedConvolutionalNetworks
https://github.com/azyner/radip
Naturalistic Driver Intention and Path Prediction using Recurrent Neural Networks
https://github.com/b8goal/PIRL-Residual-network
https://github.com/bailvwangzi/repulsion_loss_ssd
https://github.com/baklawe/CoBe
CoBe -- Coded Beacons for Localization, Object Tracking, and SLAM Augmentation
https://github.com/bamos/densenet.pytorch
https://github.com/banben/maskrcnn-benchmark
https://github.com/bareblackfoot/faster-rcnn.selection
https://github.com/bareblackfoot/lddp-tf-faster-rcnn
https://github.com/barmayo/spatial_attention
https://github.com/basaltzhang/maskrcnn-benchmark
https://github.com/bbejczy/polarseg-kitti
PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation
https://github.com/bborja/wasr_network
A water-obstacle separation and refinement network for unmanned surface vehicles
https://github.com/bclwan/MRI_Brain_Segmentation
https://github.com/beltransen/lidar_bev
BirdNet: a 3D Object Detection Framework from LiDAR information
https://github.com/ben-davidson-6/fixup
https://github.com/benedictquartey/RollE
https://github.com/benihime91/pytorch_retinanet
https://github.com/benjamintli/darknet-gun-detector
https://github.com/benjamintli/training-results-azure
https://github.com/bentoml/openllm
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
https://github.com/bentrevett/pytorch-image-classification
https://github.com/berkcomba/Self-Driving-Car-Keras
https://github.com/berkeleyflow/flow
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.