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model test problem #13
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Is there any trick I have neglected? |
The result look strange. Make sure you can run the notebook as-is and get correct results, before you make modifications. |
The code is same as what you show in notebook, thus I cannot find where the code is wrong, can you give me some guidance? thank you |
I met the same problem with you, did you find out? I would appriciate if you can share your way out. @manutdzou |
Thats great news @manutdzou . You are more than welcome to write a pull request and offer your trained model to the public. Just upload your model to a public filehoster and modify the readme with the link and your name. |
Wow I got the same strange result as your first result. Then I'm sure this released model is not so good. Anyway I rebuild U-Net on TensorFlow, my prediction result is not so good but not strange. |
@manutdzou . Hi guys, can you share your code? |
Hey Everyone, |
@PatrickChrist Hi Patrick, thanks for the great work, but when I try to use the pretrained model, I find that the nvidia-docker is hard to install and could you please share a correct pretrained model without using nvidia-docker |
@zakizhou I think because this is a reproducibility issue, Docker is our best bet to achieve that.
If you're running on linux distro, what are the issues you're facing to install The models are also shared in https://github.com/IBBM/Cascaded-FCN/tree/master/models/cascadedfcn , you can use them in your host environment (without Docker) |
@mohamed-ezz thanks for your reply, I am using ubuntu with no gpus, indeed I have tried |
Yes it's likely the caffe version. Please use the docker image.
…On Jul 4, 2017 5:45 PM, "Jie Zhou" ***@***.***> wrote:
@mohamed-ezz <https://github.com/mohamed-ezz> thanks for your reply, I am
using ubuntu with no gpus, indeed I have tried docker instead of
nvidia-docker but sadly when I tried to import pretrained caffe model,
the core of jupyter notebook dumped and I don't understand why. Like what
@manutdzou <https://github.com/manutdzou> said in this issue, the
pretrained model here https://github.com/IBBM/Cascaded-FCN/tree/master/
models/cascadedfcn performs badly on the sample image. I installed caffe
with conda, do you think it's the wrong version of caffe that caused this
problem?
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@mohamed-ezz OK, I'd try the model on a server with gpu, thanks again! |
No need for a gpu, just use docker with the image in the README.md, instead
of nvidia-docker.
…On Jul 4, 2017 6:59 PM, "Jie Zhou" ***@***.***> wrote:
@mohamed-ezz <https://github.com/mohamed-ezz> OK, I'd try the model on a
server with gpu, thanks again!
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I have released a version of right liver and lesion model in Baidu can use this model like this `import sys,os result_path = "/home/zhou/zou/Cascaded-FCN/code/result/" im_list = open('test_lesion_list.txt', 'r').read().splitlines() caffe.set_mode_gpu() liver = 1
`
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layer { layer { layer { layer { layer { layer { layer { layer { layer { layer { layer { layer { layer { layer { layer { } } } } layer { |
Great work @manutdzou |
Ok, if my code and model works well, I will be glad to commit in this repo!
发自网易邮箱大师
On 07/07/2017 17:33, Patrick Christ wrote:
Great work @manutdzou
Thanks for your support. Would you mind to commit your work in this repo?
We could have a folder model-zoo/manutdzou in which you post your code as notebook and your prototxt and the links to baidu as text file? Other users will definitly appreciate. If you have a paper about your work we can also add this.
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Hi, I have tested your released model, my code is refer to your notebook. my code is
`caffe.set_mode_gpu()
caffe.set_device(2)
net_liver = caffe.Net('/home/zhou/zou/Cascaded-FCN/models/cascadedfcn/step1/step1_deploy.prototxt', '/home/zhou/zou/Cascaded-FCN/models/cas cadedfcn/step1/step1_weights.caffemodel', caffe.TEST)
img=read_dicom_series("../train_image/3Dircadb1.17/PATIENT_DICOM/")
lbl=read_liver_lesion_masks("../train_image/3Dircadb1.17/MASKS_DICOM/")
S = 90
img_p = step1_preprocess_img_slice(img[...,S])
lbl_p = preprocess_lbl_slice(lbl[...,S])
net_liver.blobs['data'].data[0,0,...] = img_p
pred = net_liver.forward()['prob'][0,1] > 0.5
plt.figure(figsize=(3*5,10))
plt.subplot(1, 3, _1)
plt.title('CT')
plt.imshow(img_p[92:-92,92:-92], 'gray')
plt.subplot(1, 3, 2)
plt.title('GT')
plt.imshow(lbl_p, 'gray')
plt.subplot(1, 3, 3)
plt.title('pred')
plt.imshow(pred, 'gray')`
but the result is very bad like this
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