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/oorrppp2/darknet-pose

Focal Loss for Dense Object Detection

https://github.com/open-edge-platform/geti

YOLOX: Exceeding YOLO Series in 2021

https://github.com/open-mmlab/mmclassification

Deep Residual Learning for Image Recognition

https://github.com/open-mmlab/mmdetection

Feature Pyramid Networks for Object Detection

https://github.com/open-mmlab/mmpose

Deep Residual Learning for Image Recognition

https://github.com/open-mmlab/mmrotate

Gliding vertex on the horizontal bounding box for multi-oriented object detection

https://github.com/open-mmlab/mmyolo

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

https://github.com/opendilab/DI-engine

DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames

https://github.com/opendilab/DI-engine/blob/main/ding/policy/coma.py

Counterfactual Multi-Agent Policy Gradients

https://github.com/opendrivelab/hdgt

HDGT: Heterogeneous Driving Graph Transformer for Multi-Agent Trajectory Prediction via Scene Encoding

https://github.com/opendrivelab/occnet

Fully Sparse 3D Occupancy Prediction

https://github.com/opendrivelab/st-p3

ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning

https://github.com/openpifpaf/openpifpaf

OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association

https://github.com/openpifpaf/openpifpaf_posetrack

OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association

https://github.com/openpifpaf/openpifpafwebdemo

OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association

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

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

https://github.com/optimalscale/detgpt

DetGPT: Detect What You Need via Reasoning

https://github.com/orbslam-project/VIORB

Visual-Inertial Monocular SLAM with Map Reuse

https://github.com/oskyhn/CNNs-Without-Borders

Deep Residual Learning for Image Recognition

https://github.com/osmr/imgclsmob

Deep Residual Learning for Image Recognition

https://github.com/otamajakusi/darknet-yolov4

YOLOv3: An Incremental Improvement

https://github.com/oulutan/Drone_FasterRCNN

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/owennju/fgrada

FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation

https://github.com/owenps/Adversarial_Generator

DeepXplore: Automated Whitebox Testing of Deep Learning Systems

https://github.com/oxygen0605/ImageClassification

Deep Residual Learning for Image Recognition

https://github.com/p-mc-grath/DMMFODS

Densely Connected Convolutional Networks

https://github.com/p3jawors/AirSim

AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles

https://github.com/pacharadanait/Densnet_project

Densely Connected Convolutional Networks

https://github.com/pallekc91/InceptionInspiredLSTMforPredNet

Inception-inspired LSTM for Next-frame Video Prediction

https://github.com/pandamax/Lane-Detection-Based-PINet

Key Points Estimation and Point Instance Segmentation Approach for Lane Detection

https://github.com/pangfumin/Learn_VIORB

Visual-Inertial Monocular SLAM with Map Reuse

https://github.com/paperswithcode/paperswithcode-client

Deep Residual Learning for Image Recognition

https://github.com/parth-shastri/YOLOv2

YOLOv3: An Incremental Improvement

https://github.com/parthjaggi/flow

Flow: A Modular Learning Framework for Mixed Autonomy Traffic

https://github.com/parviagrawal/IdenProf

Deep Residual Learning for Image Recognition

https://github.com/pascalxia/driver_attention_prediction

Predicting Driver Attention in Critical Situations

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