Clean proj-dqry repo on branch ch/dev_clean #3
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Make sure the scripts are the most recent versions and organized them with explicit names and folders organization.
Addtion of scikit-learn library to the requirements. It is required for the prediction-filter.py. Creation of requirement.in file. The requirements.txt libraries list is set to the one of the object-detector (2022-10)
Add detailed description of the workflow procedure to performe the Training and Evaluation procedure and the Prediction procedure. Work still ongoing
Remove input and ignore folders from the list
Move the file from src folder to main proj-dqry folder. Add the Copyright and License section
Update the ignored files that will not be visible anymore on the repo
Shanci-Li
reviewed
Nov 15, 2022
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/!\ The pull request was closed without actually merging commits into the master branch. |
Implement changes suggested for pull request Clean proj-dqry repo on branch ch/dev_clean. Remove empty folder input. Add config folder. Rename src to scripts. Mv prost-processing and pre-processing one level up. Store images in image folders
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I updated the repo following your suggestions. Can you have a check and let me know if you are OK for merging this branch to master? |
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The image folder in pre-processing and post-processing can be merged and put in the same level directory of 'scripts'. Btw, you need to change the quoted path in the README.md file respectively to the new folder. |
Creation of an images folder in which all the images inclluded in the README.md files are stored. The respective path to the images in the README.md files have been updated.
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I have implemented the images related suggestions. |
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Now, it is all good! Thanks for your work! |
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##What
Reorganization of the proj-dqry repository to facilitate is use and comprehension.
##Why
The previous repo organization was unclear and not up to date.
##How
Removing folder and files relative to the tpnl project. Deleting the unused scripts of the detector-interface repo. Updating the last version of the scripts from the provided version stored on the STDL kDrive.
##Testing
Workflow is running. One last issue will have to be carried out during the Prediction workflow make-prediction.py returns error as some dataset from the Training are missing. This will be improve in the future. The README.md file detail all the information and the procedure to follow.
##To Do
Try to run the workflow locally following the README.md file. Give feedbacks on what did not work and improvements to do next.