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/kmzzhang/nbi
https://github.com/kn-joshua/Modality-Balanced-Multispectral-Pedestrian-Detection
Improving Multispectral Pedestrian Detection by Addressing Modality Imbalance Problems
https://github.com/kodluyoruz-ML-bootcamp-1/BSSA
https://github.com/kohillyang/mx-detection
https://github.com/kongzhiyou/darknet-master
https://github.com/konstantinos-p/image_classification_SOTA
https://github.com/konyul/mmdet3d
Exploring Data Augmentation for Multi-Modality 3D Object Detection
https://github.com/koolhussain/Self-Driving-Car
https://github.com/kornia/kornia
https://github.com/koshian2/ResNet-MultipleFramework
https://github.com/krishnakarthi/COVID-19_Prediction
https://github.com/krishnapalS/Human-Pose-Estimation-using-ShelfNet
https://github.com/krullgit/realsense_3d_detection
PointPillars: Fast Encoders for Object Detection from Point Clouds
https://github.com/kth-rpl/dynamicmap_benchmark
https://github.com/kuangliu/pytorch-fpn
https://github.com/kujason/ip_basic
Joint 3D Proposal Generation and Object Detection from View Aggregation
https://github.com/kumgleb/Lyft_motion_prediction
One Thousand and One Hours: Self-driving Motion Prediction Dataset
https://github.com/kun-g/Scraping-Github-trending
https://github.com/kun-woo-park/Deeplearning_project_STL_10
https://github.com/kunglab/ddnn
Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges
https://github.com/kusti8/yolov3
https://github.com/kvijaykrish/Bosch_AI_VehicleClassification
https://github.com/kwantommy/fgvc6-kaggle-cassava-classification
https://github.com/kwotsin/TensorFlow-ENet
https://github.com/kyle-dorman/bayesian-neural-network-blogpost
https://github.com/kylevedder/zeroflow
https://github.com/kyunghwan/darknet_v3
https://github.com/lab-midas/med_segmentation
https://github.com/labmlai/annotated_deep_learning_paper_implementations
https://github.com/laclouis5/darknet2
https://github.com/lanmei1211/face
https://github.com/lastbasket/endo-4dgs
Learning How To Robustly Estimate Camera Pose in Endoscopic Videos
https://github.com/latentgnn/maskrcnn-benchmark-latentgnn
https://github.com/lazyn1997/YOLOX
https://github.com/lcckkkhaha/Suprise-Adequacy-Implementation
Guiding Deep Learning System Testing using Surprise Adequacy
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.