https://github.com/nibtehaz/MultiOrganSeg/tree/master/networks/3DUNetplusplus
时间: 2023-10-03 11:10:48 浏览: 36
这是一个基于3D U-Net++网络的多器官分割项目,用于医学图像分析。3D U-Net++是一种改进的3D U-Net网络,可以更好地处理医学图像中的器官分割问题。该项目的代码使用Python编写,使用了PyTorch深度学习框架。您可以使用该项目对医学图像进行多器官分割,从而提高医学图像分析的准确性和效率。
相关问题
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。
给我推荐20个比较流行的图像实例分割模型源码地址
### 回答1:
1. DeepLab v3:https://github.com/tensorflow/models/tree/master/research/deeplab
2. Mask R-CNN:https://github.com/matterport/Mask_RCNN
3. U-Net:https://github.com/zhixuhao/unet
4. PSPNet:https://github.com/hszhao/PSPNet
5. Fast R-CNN:https://github.com/rbgirshick/fast-rcnn
6. FCN:https://github.com/shelhamer/fcn.berkeleyvision.org
7. SegNet:https://github.com/alexgkendall/SegNet-Tutorial
8. YOLOv3:https://github.com/pjreddie/darknet
9. Mask R-CNN:https://github.com/matterport/Mask_RCNN
10. ENet:https://github.com/timo saarikoski/ENet-real-time-semantic-segmentation
11. ICNet:https://github.com/hszhao/ICNet
12. DenseASPP:https://github.com/lxy5513/DenseASPP
13. RefineNet:https://github.com/guosheng/refinenet
14. DeepLab v2:https://bitbucket.org/aquariusjay/deeplab-v2
15. FCIS:https://github.com/msracver/FCIS
16. GAN-based methods:https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
17. DeepLab v1:https://bitbucket.org/aquariusjay/deeplab-v1
18. BiSeNet:https://github.com/CoinCheung/BiSeNet
19. CGNet:https://github.com/liyunsheng13/CGNet
20. DANet:https://github.com/junfu1115/DANet
### 回答2:
以下是20个比较流行的图像实例分割模型的源码地址:
1. Mask R-CNN:https://github.com/matterport/Mask_RCNN
2. DeepLab:https://github.com/tensorflow/models/tree/master/research/deeplab
3. FCN:https://github.com/shelhamer/fcn.berkeleyvision.org
4. PSPNet:https://github.com/hszhao/PSPNet
5. UNet:https://github.com/milesial/Pytorch-UNet
6. ICNet:https://github.com/hszhao/ICNet
7. SharpMask:https://github.com/facebookresearch/deepmask
8. FastFCN:https://github.com/wuhuikai/FastFCN
9. RefineNet:https://github.com/DrSleep/refinenet
10. Attention U-Net:https://github.com/ozan-oktay/Attention-Gated-Networks
11. BiSeNet:https://github.com/CoinCheung/BiSeNet
12. SINet:https://github.com/JiawangBian/SINet
13. HRNet:https://github.com/HRNet/HRNet-Semantic-Segmentation
14. OhemEdgeDetection:https://github.com/akanazawa/ohem/ohem/edge_detection
15. DEXTR:https://github.com/scaelles/DEXTR-PyTorch
16. SIPMask:https://github.com/DrSleep/SIPMask
17. SETR:https://github.com/dvittr/SETRe
18. CutMix:https://github.com/clovaai/CutMix-PyTorch
19. Mask-X-RCNN:https://github.com/Joker316701882/Mask-X-RCNN
20. U-Shaped Pyramid Networks:https://github.com/definedge/U-2-Net
这些模型涵盖了当前比较热门和有效的图像实例分割算法,你可以从中选择适合你的需求和技术栈的源码进行参考和使用。
### 回答3:
以下是20个比较流行的图像实例分割模型源码地址:
1. Mask R-CNN: https://github.com/matterport/Mask_RCNN
2. DeepLab: https://github.com/tensorflow/models/tree/master/research/deeplab
3. FCN (Fully Convolutional Networks): https://github.com/shelhamer/fcn.berkeleyvision.org
4. UNet: https://github.com/milesial/Pytorch-UNet
5. PSPNet (Pyramid Scene Parsing Network): https://github.com/hszhao/PSPNet
6. U-Net++: https://github.com/MrGiovanni/UNetPlusPlus
7. HRNet (High-Resolution Networks): https://github.com/HRNet/HRNet-Semantic-Segmentation
8. BiseNet (BiSeNet: Bilateral Segmentation Network): https://github.com/CoinCheung/BiSeNet
9. DANet (Dual Attention Network): https://github.com/junjun-jiang/DANet-pytorch
10. DFANet (Deep Feature Aggregation Network): https://github.com/yilunzhang/DFANet
11. PointRend: https://github.com/facebookresearch/detectron2/tree/main/projects/PointRend
12. Attention UNet: https://github.com/liuzechun/Attention_UNet
13. LightNet: https://github.com/ansleliu/LightNet
14. MaskX-RCNN: https://github.com/liuzechun/MaskX
15. PolarMask: https://github.com/xieenze/PolarMask
16. PolyTransform: https://github.com/google-research/google-research/tree/master/polyrnn-pp
17. FCIS (Fully Convolutional Instance Segmentation): https://github.com/msracver/FCIS
18. CGNet (Context Guided Network): https://github.com/wuhuikai/CGNet
19. ShaResNet: https://github.com/Orcuslc/ShaResNet
20. OCNet (Object Context Network): https://github.com/PkuRainBow/OCNet