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Active learning with generated synthetic image for instrument segmentation

The 3 '.ipynb' script can be run in Google Colab. To run it on your Colab, paths in the following cells should be modified to fit your Google Drive path.

import sys
path_workspace = '/content/active_learning_v1'
path_source = '/content/drive/MyDrive/active_learning_ws/active_learning_v1'
sys.path.append(path_source)

Source files for UW Sinus datasets can be found in 'src/active_syn_UW_sinus'

Source files for Endovis datasets can be found in 'src/active_syn_Endovis'

import shutil
!mkdir '/content/active_learning_v1'

shutil.copy("/content/drive/MyDrive/active_learning_ws/active_learning_v1/labels.json", 
            "/content/active_learning_v1/labels.json")

!unzip -q "/content/drive/MyDrive/active_learning_ws/data/database_folder12_l.zip" -d '/content/active_learning_v1'
!unzip -q "/content/drive/MyDrive/active_learning_ws/data/background_for_syn_folder12_l.zip" -d '/content/active_learning_v1'
!unzip -q "/content/drive/MyDrive/active_learning_ws/active_learning_v1/mobilenet_v1_1.0_224.zip" -d '/content/active_learning_v1'

Link to UW Sinus Live/Cadaver dataset

Link to EndoVis 2017 dataset

Link to mobilenet_v1_1.0_224

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