推荐10个以上比较好的SLAM深度学习模型github源码?
时间: 2023-02-05 20:09:56 浏览: 226
以下是几个比较知名的 SLAM 深度学习模型 GitHub 源码:
1. ORB-SLAM2:https://github.com/raulmur/ORB_SLAM2
2. DSO:https://github.com/JakobEngel/dso
3. LS-SLAM:https://github.com/taketwo/LS-SLAM
4. R-Net:https://github.com/HKUST-Aerial-Robotics/R-Net
5. DS-SLAM:https://github.com/HKUST-Aerial-Robotics/DS-SLAM
6. DeepVO:https://github.com/dalmia/deepvo
7. S-PTAM:https://github.com/lrse/sptam
8. ViSLAM:https://github.com/HKUST-Aerial-Robotics/VINS-Mono
9. Dense-VO:https://github.com/taketwo/dense-vo
10. SURF-SLAM:https://github.com/taketwo/surf-slam
这些模型都是使用深度学习技术来进行 SLAM 的,您可以根据自己的需要选择适合的模型进行学习和实践。
相关问题
推荐40个以上比较好的目标检测模型github源码?
我可以推荐一些比较受欢迎的目标检测模型的 GitHub 源码:
1. YOLOv3:https://github.com/pjreddie/darknet
2. Faster R-CNN:https://github.com/rbgirshick/py-faster-rcnn
3. Mask R-CNN:https://github.com/matterport/Mask_RCNN
4. R-FCN:https://github.com/daijifeng001/R-FCN
5. SSD:https://github.com/weiliu89/caffe/tree/ssd
6. RetinaNet:https://github.com/fizyr/keras-retinanet
7. Densebox:https://github.com/RuiGaoLab/DenseBox
8. YOLOv4:https://github.com/AlexeyAB/darknet
9. EfficientDet:https://github.com/google/automl/tree/master/efficientdet
10. CornerNet:https://github.com/princeton-vl/CornerNet
11. CenterNet:https://github.com/xingyizhou/CenterNet
12. CondInst:https://github.com/tianzhi0549/CondInst
13. Deformable ConvNets:https://github.com/msracver/Deformable-ConvNets
14. FCOS:https://github.com/tianzhi0549/FCOS
15. RepPoints:https://github.com/tianzhi0549/RepPoints
16. Grid R-CNN:https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn
17. FPN:https://github.com/open-mmlab/mmdetection/tree/master/configs/fpn
18. Cascade R-CNN:https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn
19. Hybrid Task Cascade:https://github.com/open-mmlab/mmdetection/tree/master/configs/htc
20. Mask Scoring R-CNN:https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_scoring_rcnn
这仅仅是一个推荐列表,并不是所有的目标检测模型。希望这些资源能够帮到你。
推荐20个以上比较好的目标检测模型github源码?
在 GitHub 上有很多优秀的目标检测模型源码,这里列出几个比较流行的:
1. YOLO (You Only Look Once):https://github.com/pjreddie/darknet
2. SSD (Single Shot MultiBox Detector):https://github.com/weiliu89/caffe/tree/ssd
3. Faster R-CNN:https://github.com/rbgirshick/py-faster-rcnn
4. Mask R-CNN:https://github.com/matterport/Mask_RCNN
5. RetinaNet:https://github.com/fizyr/keras-retinanet
6. FPN (Feature Pyramid Network):https://github.com/open-mmlab/mmdetection
7. R-FCN (Region-based Fully Convolutional Network):https://github.com/daijifeng001/R-FCN
8. DenseBox:https://github.com/Densebox/Densebox
9. HyperFace:https://github.com/MVIG-SJTU/HyperFace
10. DeepID-Net:https://github.com/DeepID/DeepID-Net
11. R-CNN (Regions with Convolutional Neural Network):https://github.com/rbgirshick/rcnn
12. Fast R-CNN:https://github.com/rbgirshick/fast-rcnn
13. G-RMI:https://github.com/viorik/G-RMI
14. Multibox:https://github.com/weiliu89/caffe/tree/multibox
15. Multitask Cascaded Convolutional Networks (MTCNN):https://github.com/kpzhang93/MTCNN_face_detection_alignment
16. Object Detection API:https://github.com/tensorflow/models/tree/master/research/object_detection
17. YOLOv3:https://github.com/pjreddie/darknet
18. M2Det:https://github.com/qijiezhao/M2Det
19. CenterNet:https://github.com/xingyizhou/CenterNet
20. EfficientDet:https://github.com/google/automl/tree/master/efficientdet
这些模型的性能都很不错,你可以根据自己的需求和计算资源选择一个适合自己的模型。