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iG-LIO_SAM_LC
iG-LIO with Loop Closure(PGO) and Online Re-LocalizeVoxelMapPlus_FASTLIO2
FAST-LIO 2 with VoxelMapPlus and STDFASTLIO2_ROS2
ROS2 / FAST_LIO / PGO / Online Re-Localization / Consistent Map with BA or HBAFASTLIO2_SAM_LC
fastlio2 with loop closure and online localizationVoxelMap_LIO_SAM
FASTLIO2 based on Voxel Mapsparse_rcnnv1
Sparse R-CNN 38.9 mAP, 640px(max side), 30.95fps(RTX 2080TI)atssv1
ATSS (retina) 39.6mAP on COCO,640px(max side),42.95fps(RTX 2080TI)<<Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection>>retinanetv1
pytorch implement of retinanet,37.4 mAp(coco) at 640px(max side) ,44.24fps(RTX2080TI)simple_pose
Some Top-Down 2D Pose Estimationcascade_rcnn
pytorch implement of CascadeRCNN,736px(max side),41.2mAP(COCO),21.94fps(RTX 2080TI)auto_assignv2
An unofficial pytorch implementation of <<AutoAssign: Differentiable Label Assignment for Dense Object Detection>>M_FASTLIVO
FAST-LIVO easy to read, esay to understandfree_anchorv1
pytorch implement of FreeAnchor(not strict),640 px(max side),39.5mAP on COCO,43.18fps(RTX 2080TI)yolo_seriesv1
YOLOv4/v5, clean code, good performance,easy to understand.gfocal
GFocal 40.6mAP(COCO) 640px(max side) 44.05 fps(rtx 2080ti)faster_rcnnv1
pytorch implement of fasterRCNN,736px(max side),39.4mAP(COCO),30.21fps(RTX 2080TI)maskrcnn
pytorch mask rcnn 736pxοΌbox mAP 39.0, seg mAP 33.7 (COCO val) 23.64fps(RTX 2080Ti)YOLO_Pedestrian
efficientdet
paav1
PAA ,resnet50+fpn,640px(max side),41.4mAP,43.68fps(RTX 2080TI)<<Probabilistic Anchor Assignment with IoU Prediction for Object Detection>>detr
M_VINS
VINS_Mono FOR ros2scaled_yolo
pointpillar3d
lidar based 3d dectectioncondinst
monodepth_v2
MonoDepthv2solov2
light_detection
light-weight detectionSCDepthV1
SCDepthV1 unsupervised mono depth estimator with better performanceLove Open Source and this site? Check out how you can help us