Comments (9)
If your validation data does not have labels then you do the split on your training data. Once the model is trained you can predict it the same way I predicted the BraTS test data
from brats2017.
OK thankyou
from brats2017.
Sorry I am still a little confused that: it seems that your file validation_network.py does the cross-validation process, so I don't need to split the training set myself?
from brats2017.
Hi
Can I train the network on a NVIDIA Geforce GTX 970 GPU 4GB and RAM of 8GB ...
from brats2017.
No you can't. It needs 12 GB of GPU memory
from brats2017.
Suppose if i reduce the no. of images and then train it batch by batch then in that case can I do it...
from brats2017.
Sir,
I meant that, I will be taking only half of the HGG data train it and then in the next training I will take the remaining half and then go for prediction. Can I do it that way....
from brats2017.
That is not how this works. The number of training cases has no effect on GPU Memory consumption. Batch size, number of filters and patch size are the ones that determine GPU memory usage. My method is designed to use large input patches, so reducing that would not be a good idea (it would defeat the purpose). batch size y 2 (i am using 2 here) is too unstable so the only thing you could do is reduce the number of filters which is a bad idea (they are already quite low in this implementation). I am very sorry, but you cannot use this method with only 4 GB of GPU memory
from brats2017.
oh ok... thnx for the inputs sir...
from brats2017.
Related Issues (20)
- Questions about mlp and hidden layers in paper/code HOT 1
- Why transpose the seg? HOT 1
- Cross validation/Generating Data HOT 1
- How can it be transformed into dimension 5 in the line 139:"l = DimshuffleLayer(l, (0, 2, 3, 4, 1))" which seems to increace dimension batch HOT 6
- A question about the dice loss HOT 2
- Different data_gen_train in different eporchs HOT 2
- /batchgenerators/batchgenerators/augmentations/crop_and_pad_augmentations.py:140: UserWarning: Raause the crop along with the dasired margin does not fit the data. data: (1, 4, 160, 192, 160), crop_s4.0] str(margins))) HOT 5
- Can I accelerate the training course with GPU? HOT 1
- Why are convolution layer and nonlinear layer seperate? HOT 1
- The patch size in the create_data_gen_train function HOT 2
- Why reshape the y_true and y_pred in the function soft_dice_per_img_in_batch HOT 2
- About the validation and test HOT 2
- How to change the predicted image into the original shape? HOT 2
- Why to get prediction this way HOT 2
- Why pad the image into a shape whose sizes are multiple of 16 before put it in the prediction function HOT 2
- Why could a multiclass dice loss function solve the class imbalance problem? HOT 2
- Preprocessing: Bias field correction HOT 2
- Docker container of BraTS 2017
- Questions about your Paper HOT 1
- Question: No New Net (2019) HOT 2
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from brats2017.