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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/MarvinTeichmann/KittiBox

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

https://github.com/MarvinTeichmann/KittiSeg

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

https://github.com/Masao-Taketani/FOTS_OCR

Deep Residual Learning for Image Recognition

https://github.com/Maskify/darknet

Focal Loss for Dense Object Detection

https://github.com/MatthewHowe/WIBAM

Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data

https://github.com/MaximeBataille/tennis_tracking

YOLOv3: An Incremental Improvement

https://github.com/MaxwellHeller/coffee_fraud_detection

Deep Residual Learning for Image Recognition

https://github.com/MaybeShewill-CV/MNN-LaneNet

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

https://github.com/MaybeShewill-CV/lanenet-lane-detection

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

https://github.com/Mayurji/Image-Classification-PyTorch

Deep Residual Learning for Image Recognition

https://github.com/Meffazm/Lane-Detector

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

https://github.com/MegEngine/Models/tree/master/official/vision/classification/resnet

Deep Residual Learning for Image Recognition

https://github.com/MegEngine/Models/tree/master/official/vision/detection

Focal Loss for Dense Object Detection

https://github.com/MegEngine/YOLOX

YOLOX: Exceeding YOLO Series in 2021

https://github.com/Mehul27/Self-Driving-Car

End to End Learning for Self-Driving Cars

https://github.com/Mersive-Technologies/yolov3

YOLOv3: An Incremental Improvement

https://github.com/MiX-S/resnet_cifar10

Deep Residual Learning for Image Recognition

https://github.com/MichaelWangfc/distributed-tensorflow-resnet

Deep Residual Learning for Image Recognition

https://github.com/MikelBros/Yolov3_DeepSort_Pytorch

YOLOv3: An Incremental Improvement

https://github.com/Mind23-2/MindCode-101/tree/main/OctSqueeze

OctSqueeze: Octree-Structured Entropy Model for LiDAR Compression

https://github.com/Mind23-2/MindCode-101/tree/main/lresnet100e_ir

Deep Residual Learning for Image Recognition

https://github.com/Mind23-2/MindCode-127

YOLOv3: An Incremental Improvement

https://github.com/Mind23-2/MindCode-129

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

https://github.com/Mind23-2/MindCode-176

Focal Loss for Dense Object Detection

https://github.com/Mind23-2/MindCode-180

Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving

https://github.com/Mind23-2/MindCode-21

LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention

https://github.com/Mind23-2/MindCode-23

LENet: Lightweight And Efficient LiDAR Semantic Segmentation Using Multi-Scale Convolution Attention

https://github.com/Mind23-2/MindCode-3/tree/main/lresnet100e_ir

Deep Residual Learning for Image Recognition

https://github.com/Mind23-2/MindCode-46

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Mind23-2/MindCode-47

FCOS: Fully Convolutional One-Stage Object Detection

https://github.com/Mind23-2/MindCode-67

Deep Residual Learning for Image Recognition

https://github.com/Mind23-2/MindCode-70

Deep Residual Learning for Image Recognition

https://github.com/MindCode-4/code-12/tree/main/multimodal-trajectory

Multimodal Motion Prediction with Stacked Transformers

https://github.com/MindCode-4/code-3/tree/main/flava

FLAVA: Find, Localize, Adjust and Verify to Annotate LiDAR-Based Point Clouds

https://github.com/MindCode-4/code-4/tree/main/rembert

Deep Residual Learning for Image Recognition

https://github.com/MindCode-4/code-8/tree/main/multimodal-trajectory

Multimodal Trajectory Predictions for Autonomous Driving using Deep Convolutional Networks

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