Comments (3)
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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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
- Blank prediction results HOT 1
- 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
- 2D data HOT 3
- 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
- prediction failed (octave error) HOT 6
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