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Improved UNet by s4627234 #394
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DaveD123321
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Improved UNet by s4627234 #394
DaveD123321
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23
commits into
shakes76:topic-recognition
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DaveD123321:topic-recognition
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…on into predict.py fixing some tensorflow version bugs from modules.py
…ion in dataset, adding comments
…y image into train.py add save and load model function
Collaborator
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This is an initial inspection, no action is required at this point
IMPORTANT: Commits are not in the right place. Did you create the topic-recognition branch yourself? In your PR update, please fork Shakes repo in whole (there is a checkmark when you fork) and commit to his topic-recognition branch. You should be able to see work from the previous years in "recognition folder" |
Author
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I used this email to reply just in case, I received a question asking why it was submitted late, but it seems that previous issue can only be deleted and re-build a new branch. The screenshot is my corresponding new feedback.
…________________________________
From: SiyuLiu0329 ***@***.***>
Sent: Monday, November 14, 2022 2:40:56 PM
To: shakes76/PatternFlow ***@***.***>
Cc: DaveD123321 ***@***.***>; Author ***@***.***>
Subject: Re: [shakes76/PatternFlow] Improved UNet by s4627234 (PR #394)
This is an initial inspection, no action is required at this point
* Code: OK
* Results: OK
* Readme: OK
* Commit Messages: OK
* Other comments: N/A
IMPORTANT: Commits are not in the right place. Did you create the topic-recognition branch yourself? In your PR update, please fork Shakes repo in whole (there is a checkmark when you fork) and commit to his topic-recognition branch. You should be able to see work from the previous years in "recognition folder"
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Reply to this email directly, view it on GitHub<#394 (comment)>, or unsubscribe<https://github.com/notifications/unsubscribe-auth/AYD3CFRCSTWIVSVRDK5QBTTWIG7FRANCNFSM6AAAAAARJ3YG6A>.
You are receiving this because you authored the thread.Message ID: ***@***.***>
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Author: Jingming Dai
Student Number: s4627234 / 46272346
Description: Q1 in the COMP3710 report - ISIC dataset segmentation based on improved Unet.
Files :
modules.pysource code of the components of model.dataset.pydata loader for loading and preprocessing data.train.pysource code for training, testing, validating, and saving model.predict.pyshowing example of your model base on validation data.README.MDdocument of projectimagesimages of the results produced by the model