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/hyzhou404/hugsim

HUGSIM: A Real-Time, Photo-Realistic and Closed-Loop Simulator for Autonomous Driving

https://github.com/hz2538/iot_JennyGo

Deep Residual Learning for Image Recognition

https://github.com/iArunava/ResNet

Deep Residual Learning for Image Recognition

https://github.com/iPhoring/BehavioralCloning

End to End Learning for Self-Driving Cars

https://github.com/iac-team/semseg

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

https://github.com/iamsingh/darknet

YOLOv3: An Incremental Improvement

https://github.com/ibaiGorordo/ONNX-HybridNets-Multitask-Road-Detection

HybridNets: End-to-End Perception Network

https://github.com/ibaiGorordo/ONNX-LSTR-Lane-Detection

End-to-end Lane Shape Prediction with Transformers

https://github.com/ibaiGorordo/ONNX-YOLOv7-Object-Detection

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

https://github.com/ibaiGorordo/PyTorch-High-Res-Stereo-Depth-Estimation

Hierarchical Deep Stereo Matching on High-resolution Images

https://github.com/ibaiGorordo/TfLite-Ultra-Fast-Lane-Detection-Inference

Ultra Fast Structure-aware Deep Lane Detection

https://github.com/ibaiGorordo/onnx-Ultra-Fast-Lane-Detection-Inference

Ultra Fast Structure-aware Deep Lane Detection

https://github.com/iberganzo/darknet

YOLOv3: An Incremental Improvement

https://github.com/idiap/eslam

ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields

https://github.com/idobronstein/vision_networks

Densely Connected Convolutional Networks

https://github.com/idsia-robotics/RandLA-Net-pytorch

Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset

https://github.com/idthanm/env_build

Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving Intelligence

https://github.com/ihamdi/Covid-xRay-Classification

Densely Connected Convolutional Networks

https://github.com/iitmcvg/redtail

On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach

https://github.com/ikhlestov/vision_networks

Densely Connected Convolutional Networks

https://github.com/imkoushik22/Walmart-Hackathon

Deep Residual Learning for Image Recognition

https://github.com/imshamsher/darknet

YOLOv3: An Incremental Improvement

https://github.com/inhwanbae/npsn

Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

https://github.com/insigh/Faster-RCNN-Tensorflow

Feature Pyramid Networks for Object Detection

https://github.com/intel/ad-rss-lib

On a Formal Model of Safe and Scalable Self-driving Cars

https://github.com/interestingzhuo/foodretreival

Deep Residual Learning for Image Recognition

https://github.com/inverted-ai/torchdrivesim

Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation

https://github.com/irh-ca-team-car/attention-data

BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

https://github.com/isheunesutembo/TB-Computer-Aided-Diagnosis-Using-Deep-Learning

Deep Residual Learning for Image Recognition

https://github.com/iskandari/darknet

Focal Loss for Dense Object Detection

https://github.com/isl-org/Open3D-ML

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/istvanmegyeri/audio_tagging

Focal Loss for Dense Object Detection

https://github.com/isunLt/PolarSeg

PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation

https://github.com/ithuanhuan/gpu-py3-faster-rcnn

Feature Pyramid Networks for Object Detection

https://github.com/ithuanhuan/py-fatser-rcnn

Feature Pyramid Networks for Object Detection

https://github.com/itijyou/ademxapp

Deep Residual Learning for Image Recognition

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