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/nmcasasr/darknet2

YOLOv3: An Incremental Improvement

https://github.com/nmcasasr/darknet_oldVersion

YOLOv3: An Incremental Improvement

https://github.com/noahfl/densenet-sdr

Densely Connected Convolutional Networks

https://github.com/northeastsquare/effficientnet

Deep Residual Learning for Image Recognition

https://github.com/notmariekondo/notmariekondo

YOLOP: You Only Look Once for Panoptic Driving Perception

https://github.com/nqt228/YoloZ

YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles

https://github.com/nrhine1/precog_carla_dataset

PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings

https://github.com/ns15417/LearnVIORB-RGBD

Visual-Inertial Monocular SLAM with Map Reuse

https://github.com/ns15417/multicol-slam2

MultiCol-SLAM - A Modular Real-Time Multi-Camera SLAM System

https://github.com/nsinghal7/Kimera-VIO-NetVLAD

Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping

https://github.com/nsinghal7/Kimera-VIO-ROS-NetVLAD

Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping

https://github.com/nsom/Basic-Models

Deep Residual Learning for Image Recognition

https://github.com/nsom/VGG16

Deep Residual Learning for Image Recognition

https://github.com/ntrin/CIFAR_10_ML

Deep Residual Learning for Image Recognition

https://github.com/nurulxakmar/darknet

YOLOv3: An Incremental Improvement

https://github.com/nv-tlabs/planning-centric-metrics

The efficacy of Neural Planning Metrics: A meta-analysis of PKL on nuScenes

https://github.com/nvidia-ai-iot/lidar_ai_solution

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/nvlabs/adaptive-prediction

Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data

https://github.com/nvlabs/catk

Wayformer: Motion Forecasting via Simple & Efficient Attention Networks

https://github.com/nvlabs/parametricbev

Parametric Depth Based Feature Representation Learning for Object Detection and Segmentation in Bird's Eye View

https://github.com/nvlabs/traffic-behavior-simulation

BITS: Bi-level Imitation for Traffic Simulation

https://github.com/nvlabs/vila

AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

https://github.com/nvr-avg/adaptive-prediction

Trajectron++: Dynamically-Feasible Trajectory Forecasting With Heterogeneous Data

https://github.com/nvr-avg/trajdata

trajdata: A Unified Interface to Multiple Human Trajectory Datasets

https://github.com/nyoki-mtl/pytorch-discriminative-loss

Semantic Instance Segmentation with a Discriminative Loss Function

https://github.com/obo/lego

Image Based Camera Localization: an Overview

https://github.com/ogatarina-sq/secyolov7

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

https://github.com/okason97/DenseNet-Tensorflow2

Densely Connected Convolutional Networks

https://github.com/okason97/handshape-recognition

Densely Connected Convolutional Networks

https://github.com/okboseong/darknet

YOLOv3: An Incremental Improvement

https://github.com/olivesgatech/CURE-TSR

CURE-TSR: Challenging Unreal and Real Environments for Traffic Sign Recognition

https://github.com/ollewelin/libtorch-GPU-CNN-test-MNIST-with-Batchnorm

Deep Residual Learning for Image Recognition

https://github.com/olly-styles/Dynamic-Trajectory-Predictor

Forecasting Pedestrian Trajectory with Machine-Annotated Training Data

https://github.com/oloocki/scan2lod3

Scan2LoD3: Reconstructing semantic 3D building models at LoD3 using ray casting and Bayesian networks

https://github.com/omni-us/squeezedet-keras

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

https://github.com/omron-sinicx/planning-datasets

OpenTraj: Assessing Prediction Complexity in Human Trajectories Datasets

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