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/euske/miniyolo
https://github.com/evantkchong/LEPAR
Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method
https://github.com/ewinata/CIFAR-10
https://github.com/f1yfisher/drivedreamer2
DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generation
https://github.com/fL0n9/SKFAC-MindSpore
https://github.com/fabhertz95/HATS
HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification
https://github.com/fabiofumarola/ultrayolo
https://github.com/fabxy/accessibilityCV
https://github.com/facebook/fb.resnet.torch
https://github.com/facebookarchive/fb.resnet.torch
https://github.com/facebookresearch/DomainBed
https://github.com/facebookresearch/SegmPred
https://github.com/facebookresearch/detectron2
https://github.com/facebookresearch/habitat-api/tree/master/habitat_baselines/rl/ddppo
DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames
https://github.com/facebookresearch/hiera
https://github.com/facebookresearch/pycls
https://github.com/facebookresearch/pytorch3d
https://github.com/facebookresearch/stochastic_gradient_push
https://github.com/facundoq/transformational_measures_experiments
https://github.com/fangchangma/sparse-to-dense.pytorch
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
https://github.com/fastai/fastai
https://github.com/fcakyon/sahi-benchmark
https://github.com/fchollet/deep-learning-models
https://github.com/feibryantkit/lf-tracy
OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation
https://github.com/feidfoe/AdjustBnd4Imbalance
https://github.com/feixue94/pram
PRAM: Place Recognition Anywhere Model for Efficient Visual Localization
https://github.com/felixgwu/img_classification_pk_pytorch
https://github.com/feng-lab/nuclei
https://github.com/fengjiqiang/pretrainedmodel_pytorch
https://github.com/fengredrum/cnn-xla
https://github.com/fi94/yolov3
https://github.com/fictivename/cesor_ppo
https://github.com/filippogiruzzi/semantic_segmentation
https://github.com/firja10/darknet-2
https://github.com/fizyr/keras-retinanet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.