Papers with Code 아카이브
Autonomous Vehicles
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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/AbangLZU/lanenet-lane-detection-py2
Towards End-to-End Lane Detection: an Instance Segmentation Approach
https://github.com/Abhaya1998/Self-Driving-Car
https://github.com/Abhijeet2396/darknet
https://github.com/AbhinavJhanwar/face_detection
https://github.com/AbhinavJhanwar/object-detection-classification
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
https://github.com/Adam123wu/Edge-YOLO_algorithm
https://github.com/AdeelH/pytorch-multi-class-focal-loss
https://github.com/Adelaide-AI-Group/FCOS
https://github.com/Adelaide-AI-Group/G2D
https://github.com/AdolfoMatias/rede_neural_laranja_maca_adolfo
https://github.com/AdvAttack/TAA-IoT-J
Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognition
https://github.com/AdvancedAI-ComplexSystem/SmartCity
Efficient Pressure: Improving efficiency for signalized intersections
https://github.com/AeroZealous4/Self_Driving_Car_CNN
https://github.com/AhmadWaly/Endtoend-learning-for-self-driving-cars
https://github.com/Ahsanr312/Object-Detection-and-Tracking-using-YOLOv3-and-DeepSort
https://github.com/Ahsanr312/Object-Detection-in-Satellite-Imagery-Using-YOLOv3-
https://github.com/AirBernard/DenseNet-by-Pytorch
https://github.com/AishrithRao/robust-physical-attack
ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector
https://github.com/Aizierjiang/mr
Generative AI-empowered Simulation for Autonomous Driving in Vehicular Mixed Reality Metaverses
https://github.com/AjJordy/squeezeDet-master
https://github.com/Akilax0/Pseudo-RGB-D-for-Self-Improving-Monocular-SLAM-and-Depth-Prediction
Pseudo RGB-D for Self-Improving Monocular SLAM and Depth Prediction
https://github.com/AlbertFeng-0405/SceneTransformer-pytorch
Scene Transformer: A unified architecture for predicting multiple agent trajectories
https://github.com/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection
Robust and Efficient Post-Processing for Video Object Detection (REPP)
https://github.com/AlexeyAB/darknet
https://github.com/AlexeyZhuravlev/visual-backprop
https://github.com/AllenPeng0209/deeproute_competition
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/AlphaJia/tf-faster-rcnn
https://github.com/AmeerHamza111/yolo-darknet
https://github.com/AmirNiaraki/Visual-Exploration
Visual Exploration and Energy-aware Path Planning via Reinforcement Learning
https://github.com/AmitNativ1984/yolov3
https://github.com/AmrElsersy/PointPainting
https://github.com/Anderies/stochastic-constant-pruninglayers
https://github.com/Andreas-Pfeuffer/LSTM-ICNet
https://github.com/AndrewGavril/Kimera-VIO-RaspberryPi
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
https://github.com/AndreydeAguiarSalvi/yolo_compression
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.