Papers with Code 아카이브

Autonomous Vehicles

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하위 태스크: 15
평가표: 1
보존된 평가 기록: 2

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/naimaryan1/KD_TREE

Deterministic Iteratively Built KD-Tree with KNN Search for Exact Applications

https://github.com/naman-mehta/predict_steering_angle

End to End Learning for Self-Driving Cars

https://github.com/namdvt/Focal-loss-pytorch-implementation

Focal Loss for Dense Object Detection

https://github.com/nandaswayam/Self-Driving-car

End to End Learning for Self-Driving Cars

https://github.com/nanzhaogang/contrib/tree/master/application/multimodal-trajectory-predictions-for-autonomous-driving-using-deep-convolutional-networks

Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

https://github.com/narimanabdi/seqnet

https://github.com/nashawnch/Computer-Vision-FerrariDetective

Densely Connected Convolutional Networks

https://github.com/nashawnch/ComputerVision-FerrariDetective

Densely Connected Convolutional Networks

https://github.com/nautilus261/tf-faster-rcnn

Feature Pyramid Networks for Object Detection

https://github.com/nautilusPrime/autodrive_ddpg

Learning to Drive in a Day

https://github.com/navoshta/behavioral-cloning

End to End Learning for Self-Driving Cars

https://github.com/ndb796/pytorch-adversarial-training-cifar

Deep Residual Learning for Image Recognition

https://github.com/neilctwu/FCOS-pytorch_Simplified

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/nerdimite/behavioral-cloning

End to End Learning for Self-Driving Cars

https://github.com/neshitov/UNet

Focal Loss for Dense Object Detection

https://github.com/neung678/anti_bird

YOLOv3: An Incremental Improvement

https://github.com/neuqgz/modify-faster-rcnn-tf

Feature Pyramid Networks for Object Detection

https://github.com/neurai-lab/mt-sfmlearner

Transformers in Self-Supervised Monocular Depth Estimation with Unknown Camera Intrinsics

https://github.com/neuralmagic/sparseml

Fast Algorithms for Convolutional Neural Networks

https://github.com/neuralmagic/sparsify

Fast Algorithms for Convolutional Neural Networks

https://github.com/neverleave0916/darknet

YOLOv3: An Incremental Improvement

https://github.com/newsun-boki/yolovx-openvino-c--video-infer

YOLOX: Exceeding YOLO Series in 2021

https://github.com/newsun-boki/yolox-openvino-video-infer

YOLOX: Exceeding YOLO Series in 2021

https://github.com/ngatilio/aiethics

Never trust, always verify : a roadmap for Trustworthy AI?

https://github.com/nghgphi/fire_detection

YOLOv3: An Incremental Improvement

https://github.com/ngowilliam1/centerfusion_depth_factor

CenterFusion: Center-based Radar and Camera Fusion for 3D Object Detection

https://github.com/nguoido/Yolo-alexeyAB

Focal Loss for Dense Object Detection

https://github.com/nice-hku/cl2marl-smac

Counterfactual Multi-Agent Policy Gradients

https://github.com/nicolalandro/Padam

Deep Residual Learning for Image Recognition

https://github.com/nikkkkhil/lane-detection-using-lanenet

Towards End-to-End Lane Detection: an Instance Segmentation Approach

https://github.com/nikkkkhil/self-driving-car-implementaion-based-on-nvidia-paper-using-keras

Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car

https://github.com/nikosRp/Road-Obstacle-Recognition-Using-Images-And-Machine-Learning

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

https://github.com/nilesh0109/PedestrianTracking

YOLOv3: An Incremental Improvement

https://github.com/niranjana98/Image-Classification

Densely Connected Convolutional Networks

https://github.com/nishantml/Self-Driving-Car

End to End Learning for Self-Driving Cars

https://github.com/nlee-208/face_mask_detection

YOLOv3: An Incremental Improvement

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.