Comments (5)
I think xnet is the real UNet++. nestnet doesn't connect current layer to all previous layers . Therefore,the accuracy of the nestnet is not as high as xnet.
I think nestnet is wrong. And xnet is a modified version of nestnet.
And I think there is also something wrong with xnet,even if I'm not sure.
For example,In xnet , the author reverse its order when using decoder_filters=[256 128 64 32 16].
So the number of filters the author wants to use should be [16 32 64 128 256].
But the number of filters the author actually uses is [32 64 128 256 16].
And I also found other things(I think it‘s wrong) that I don’t understand very well.
from unetplusplus.
Hey @zsk-tech thank you for your input.
I agree that the connections are different..
but that does not mean it is necessarily 'wrong' it could just be a version of Unet++ with less skip connections but still working correctly right?
What we can say is it makes a difference in the number of trainable parameters.
E.g. for resnet18:
# xnet resnet18
Total params: 18,273,898
Trainable params: 18,261,156
Non-trainable params: 12,742
# nestnet resnet18
Total params: 17,462,890
Trainable params: 17,450,148
Non-trainable params: 12,742
I was also confused by the filters of the additional decoder skip connections.
Where did you see the author using [32 64 128 256 16] number of filters?
For me when I look up the filters of the layers that are skip connections it looks like this for resnet18:
stage4_unit1_relu1 (Activation) (None, 16, 16, 256)
stage3_unit1_relu1 (Activation) (None, 32, 32, 128)
stage2_unit1_relu1 (Activation) (None, 64, 64, 64)
relu0 (Activation) (None, 128, 128, 64)
relu1 (Activation) (None, 8, 8, 512)
stage3_unit2_relu1 (Activation) (None, 16, 16, 256)
stage2_unit2_relu1 (Activation) (None, 32, 32, 128)
stage1_unit2_relu1 (Activation) (None, 64, 64, 64)
But maybe you looked into what actually happens in build_xnet
and that's why I'm confused about that statement.
from unetplusplus.
Thank you for your explanation.
I think what you said is right. Xnet and nestnet are just two different versions.
Q: Where did you see the author using [32 64 128 256 16] number of filters?
A: I derived it from the code in build_xnet.But it is also possible that my understanding of the code is incorrect.
from unetplusplus.
Okay that makes sense, I'll try to look at it when I find the time and report back!
The build_xnet
function it's not easy to unpack sadly, maybe the authors find time to answer in the meantime :)
from unetplusplus.
OKay
from unetplusplus.
Related Issues (20)
- Getting very bad validation metrics HOT 2
- how to train on my designated GPU? HOT 1
- Can this model be trained on multi GPUs ?
- importlib_metadata.PackageNotFoundError: No package metadata was found for nnunet HOT 1
- 周学长您好,我在配置环境中发现个小坑想提醒大家
- RuntimeError: Given transposed=1, weight of size [256, 128, 2, 2], expected input[12, 64, 256, 256] to have 256 channels, but got 64 channels instead HOT 2
- 您好!请问pytorch官方实现版提供的LITS预训练模型,是关闭deep_supervision跑的么 HOT 7
- Question : why use " l = weights[0] * self.loss(x[-1], y[0])" in loss_functions/deep_supervision.py ?
- Anyone here who have update this code for tensorflow version 2.x HOT 1
- issue in resize_segmentation HOT 3
- 'unet_final_features' referenced before assignment HOT 1
- Generic_UNetPlusPlus测试报错
- legacy_upsampling2d_support
- How to Train on nnunet 2d mode?
- PermissionError: [Errno 13] Permission denied: '/media/yang/nnUNet_raw_data_base'
- AttributeError: 'Generic_UNetPlusPlus' object has no attribute 'upsample_mode'
- Pre-trained model
- UNetPlusPlus如何基于自定义数据集完成实例分割任务?(而不是语义分割)
- How do I train U-Net++ on my custom 3D data?
- Help
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from unetplusplus.