Comments (3)
coleslaw481
commented on August 21, 2024
Hi,
Training can was setup to work on 100 1k x 1k images. Were you trying to run training (runtraining.sh) on the larger images or did you mean to run prediction (runprediction.sh)? Prediction shouldn't have a problem with 4k x 2k images.
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orinp
commented on August 21, 2024
HI,
Thanks for your reply.
I wanted to train on the larger images. I could reduce the xy dimensions some but the cells I am trying to segment are neurons with long dendrites and axons and span well over 100 images in z. 330 images spans this particular cell type but there are other cell types that span even more sections. And reducing the xy extent too much would reduce the number of labels for training by quite a bit. For that reason I really need to train on fairly large stacks.
I am new to working with these models so perhaps my expectations are unrealistic but for cdeep3m to work for me I probably need to train stacks of this size and to predict stacks of even larger size.
I was hoping that maybe there was a simple way to allocate more memory.
…-Orin Packer
On Dec 12, 2018, at 10:23 AM, Chris Churas ***@***.***> wrote:
Hi,
Training can was setup to work on 100 1k x 1k images. Were you trying to run training (runtraining.sh) on the larger images or did you mean to run prediction (runprediction.sh)? Prediction shouldn't have a problem with 4k x 2k images.
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haberlmatt
commented on August 21, 2024
Hi,
without knowing what your images look like its hard to recommend how training data should be organized best (If you'd like you can send an example to [email protected])
but if I understand correctly the neurons you have labeled are large in x/y and z (and therefore will be much larger than the typical size of a receptive field of deep neural networks. The issue here will become GPU memory).
I would try if you can downsample the data to about 1/4th of the size in x/y, I suspect that you will still see the axons at that scale.
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Related Issues (20)
- Using the pretrained model at /sbem/mitochrondria/xy5.9nm40nmz/30000iterations_train_out failed HOT 7
- /usr/bin/time not available on all systems
- HDF5 error with Ubuntu 18.04 LTE HOT 4
- Add validation data to training failed HOT 3
- runprediction not working on local build HOT 4
- Watershed error HOT 2
- Image binarization and exclusion zone HOT 4
- Can we apply your model on different applications? HOT 1
- checkpoint_nobinary(imgstack) HOT 14
- Unable to plot validation loss HOT 1
- caffepredict doesn't parrellelize properly HOT 1
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- Error when running runprediction.sh on a local build. Any help is appreciated! HOT 8
- How to obtain the training accuracy from the log file? HOT 4
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- architecture used? HOT 1
- dataset used in training HOT 1
- How to input muti-channel label image,such as(width*height*channel) and How to use label_class_selection.prototxt
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