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Issues: microsoft/seismic-deeplearning
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fix seeds in train.py and test.py to make results perfectly reproducible on a single GPU - enhances the robustness of results
Investment: High
how long it takes to do a work item
Investment: Medium
how long it takes to do a work item
Type: Enhancement
This an enhancement to an existing feature
add a clean-up job to remove output directories from build VM
Prior: Low
Type: Enhancement
This an enhancement to an existing feature
cache Penbscot and Dutch F3 data on build VM and download weekly
Prior: Medium
Type: Enhancement
This an enhancement to an existing feature
Add data QC module
Investment: Low
how long it takes to do a work item
Prior: High
Type: Feature
New feature or request
Dynamic global data information, reduces user-required specifications
Investment: Low
how long it takes to do a work item
Prior: Low
Type: Feature
New feature or request
add pre-trained models to test section of the HRNet notebooks - facilitates faster onboarding with more pre-trained models
Prior: Low
Type: Enhancement
This an enhancement to an existing feature
add UNet pre-trained model - facilitates faster onboarding with UNet model
Prior: Low
Type: Enhancement
This an enhancement to an existing feature
make pre-run notebooks for segyconverter utility and SEResNet available in main README - facilitates faster quick-start experience
Prior: High
Type: Enhancement
This an enhancement to an existing feature
Type: Gap
Not feature but a missing item to an existing feature
debug test.py not limiting test output in debug mode - facilitates faster build runtimes
Prior: High
Type: Bug
Something isn't working
Type: Enhancement
This an enhancement to an existing feature
debug test.py patch padding before model scoring - facilitates scoring correctness and higher scoring accuracy
Prior: Medium
Type: Accuracy
related to increasing performance accuracy
Type: Bug
Something isn't working
add unit test to check that each input slice has uniform distribution - facilitates uniform sampling of training data for better model accuracy
Prior: High
Type: Accuracy
related to increasing performance accuracy
Generated data splits should be tracked along with model outputs, and not stored with data.
Type: Enhancement
This an enhancement to an existing feature
Host DutchF3 on Azure open datasets - overcomes reliability problem of data availability
Prior: Low
Type: Enhancement
This an enhancement to an existing feature
read model parameters from configuration file for texture_net - facilitates easy model architecture changes
Prior: Low
Type: Enhancement
This an enhancement to an existing feature
make output transform unpad and scale down mask in penobscot metrics - facilitates correctly reported inline mIoU
Prior: Medium
Type: Accuracy
related to increasing performance accuracy
Type: Enhancement
This an enhancement to an existing feature
add logging to data.py methods - facilitates better logging
Prior: Low
Type: Enhancement
This an enhancement to an existing feature
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