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New Model: PialNN/pialnn/1.0.0 #102
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Branch issue-102 created for issue: New Model: PialNN/pialnn/1.0.0 |
[This is an automated message] Hello @hvgazula, 🔴 The folders/scripts you provided did not pass our tests. Please review the Action, and modify this issue's urls accordingly. When ready, simply append "Ready XX" in the issue title (where XX is a number incrementing with 01 each time a fix has been applied). |
[This is an automated message] Hello @hvgazula, 🔴 The folders/scripts you provided did not pass our tests. Please review the Action, and modify this issue's urls accordingly. When ready, simply append "Ready XX" in the issue title (where XX is a number incrementing with 01 each time a fix has been applied). |
[This is an automated message] Hello @hvgazula, 🔴 The folders/scripts you provided did not pass our tests. Please review the Action, and modify this issue's urls accordingly. When ready, simply append "Ready XX" in the issue title (where XX is a number incrementing with 01 each time a fix has been applied). |
Model's repository path
PialNN/pialnn/1.0.0
Best model weights
https://drive.google.com/uc?id=18KdBZLwd-8ld3SZoQCXAXwvrCtdeABU-&confirm=t
Docker information
https://github.com/gaiborjosue/DeepCSR-testing/tree/main/dockerP
Model's python scripts
https://github.com/gaiborjosue/DeepCSR-testing/blob/main/pial/predict.py
https://github.com/gaiborjosue/DeepCSR-testing/blob/main/pial/utils.py
https://github.com/hvgazula/pialnn-forked/tree/harsha/data
https://github.com/hvgazula/pialnn-forked/tree/harsha/model
https://github.com/gaiborjosue/DeepCSR-testing/blob/main/pial/config.py
Model's card and spec
https://github.com/gaiborjosue/trained-models-fork/blob/master/DeepCSR/deepcsr/1.0/spec.yaml
https://github.com/gaiborjosue/trained-models-fork/blob/master/DeepCSR/deepcsr/1.0/model_card.yaml
Sample data
https://hvgazula.github.io/pialnn-forked/sample.zip
Model config
No response
Test Command
python ./PialNN/pialnn/1.0.0/predict.py --model ./PialNN/pialnn/1.0.0/weights/pialnn_model_lh_200epochs.pt --data_path=./PialNN/pialnn/1.0.0/data/test/ --hemisphere=lh --save_mesh_eval=False
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