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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/yusufsugiono/klasifikasi-gambar-bunga

Densely Connected Convolutional Networks

https://github.com/yuyi1005/point2rbox-mmrotate

H2RBox: Horizontal Box Annotation is All You Need for Oriented Object Detection

https://github.com/yw0nam/DenseNet

Deep Residual Learning for Image Recognition

https://github.com/yxgeee/MMT

Deep Residual Learning for Image Recognition

https://github.com/yytang2012/darknet

Focal Loss for Dense Object Detection

https://github.com/yzgrfsy/tf-fastrcnn-crop

Feature Pyramid Networks for Object Detection

https://github.com/zad1206/simtrack-mindspore

Exploring Simple 3D Multi-Object Tracking for Autonomous Driving

https://github.com/zanmange/darknet

YOLOv3: An Incremental Improvement

https://github.com/zcc31415926/FFNet

Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward

https://github.com/zeegeeko/YOLOv3

YOLOv3: An Incremental Improvement

https://github.com/zephyrcho/darknetV4

YOLOv3: An Incremental Improvement

https://github.com/zgxsin/lidar_camera_calibration

LiDAR-Camera Calibration using 3D-3D Point correspondences

https://github.com/zh320/realtime-semantic-segmentation-pytorch

ShelfNet for Fast Semantic Segmentation

https://github.com/zhanghuiyao/yolov3_mindspore

YOLOv3: An Incremental Improvement

https://github.com/zhanghuiyao/yolov7_mindspore

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

https://github.com/zhangjy2008327/lane-detection-with-double-convgrus

Lane Detection Model Based on Spatio-Temporal Network With Double Convolutional Gated Recurrent Units

https://github.com/zhangming8/libtorch-yolov3

YOLOv3: An Incremental Improvement

https://github.com/zhangming8/yolox-pytorch

YOLOX: Exceeding YOLO Series in 2021

https://github.com/zhaozhijie1997/Unifed-Lane-and-Traffic-Sign-detection

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/zhejz/trafficbotsv1.5

Scene Transformer: A unified architecture for predicting multiple agent trajectories

https://github.com/zhengyinan-air/diffusion-planner

Diffusion-Based Planning for Autonomous Driving with Flexible Guidance

https://github.com/zhenye-na/e2e-learning-self-driving-cars

End to End Learning for Self-Driving Cars

https://github.com/zhixinlai/llmboostmedical

Frozen Transformers in Language Models Are Effective Visual Encoder Layers

https://github.com/zhongzisha/object_detection

Feature Pyramid Networks for Object Detection

https://github.com/zhubinQAQ/Ins

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/zhulf0804/pointpillars

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/zhuliwen/pku_traffic_lights

MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control

https://github.com/zhyever/PatchFusion

PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth Estimation

https://github.com/zhyever/patchrefiner

PatchRefiner: Leveraging Synthetic Data for Real-Domain High-Resolution Monocular Metric Depth Estimation

https://github.com/zijianh4/CROP-leaderboard.github.io

CROP: Certifying Robust Policies for Reinforcement Learning through Functional Smoothing

https://github.com/zion-king/Center-based-3D-Object-Detection-and-Tracking

Center-based 3D Object Detection and Tracking

https://github.com/ziyadsheeba/qfat

Behavior Generation with Latent Actions

https://github.com/ziyuan400/video_segmentation

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

https://github.com/zj463261929/darknet_mAP

Focal Loss for Dense Object Detection

https://github.com/zjZSTU/ResNet

Deep Residual Learning for Image Recognition

https://github.com/zjjszj/yolov3

YOLOv3: An Incremental Improvement

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