Comments (3)
I used the original configuration file and the corresponding parameters, but the score on the verification set was (0.75315 0.90392 0.80955).
I'd like to ask the same question.
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The pre-trained models released here are not exactly the ones I used in the paper. To release the repository, I re-organized the code to make it clearer to understand, then I re-trained the model. However, the re-training was implemented on another GPU with 6GB memory so I reduced the batch size. This caused a performance decrease.
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@taigw Thank you for your reply! When I trained the model, I set the batch_size to 3 and keep all other options unchanged in the configure files. And the result I got is (0.7376, 0.8997, 0.7912) after 20000 iterations, it's still lower than the result tested by the pre-trained model you gave, I don't know why the model I trained can't reproduce the result of your‘s, is there anything wrong with what I didn't pay attention to?
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Related Issues (20)
- Regarding subimage patch and label size HOT 7
- ERROR (nifti_image_write_hdr_img2): cannot open output file HOT 11
- How to use multi-gpu? HOT 1
- Do I have to rewrite test.py when I define the network without NiftyNet? HOT 1
- About the size of data_shape HOT 9
- UnboundedLocalError: local variable referenced before assignment.
- Activation Layer Before Convolution layer in ResBlock HOT 1
- net1 output is null HOT 2
- Can I stop training early?
- Some questions about data_root
- test_one_image_three_nets_adaptive_shape function
- Config File Generation for brats 2018 Data
- Overlap in Train/Test Data
- How to enable NiftyNet's balanced window sampler?
- loss is not declined HOT 1
- OOM about training with brats17 data HOT 1
- Error: Provided indices are out-of-bounds
- No module named 'util'
- Where can we find the labels for Brats 2015 dataset?
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