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Enhancing Model Accuracy through Advanced Transfer Learning Technique #13

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merged 4 commits into from
Oct 4, 2024

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DarshAgrawal14
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Issue : #8

  • The current implementation utilizes EfficientNet for terrain classification.
  • Data Agumentation includes resizing, horizontal flipping, color jitter, and random rotations.
  • I have loaded a pre-trained EfficientNet B0 model and modified the classifier layer to match the number of output classes (5 for different terrain types).
  • I noticed at high learning rates the accuracy was highly volatile so it was lowered
  • I defined the loss function (CrossEntropyLoss) and optimizer (Adam) with a lower learning rate for fine-tuning.
  • Learning rate scheduling : ReduceLROnPlateau
  • Included code to load model and test it using a example image
  • The hyper - parameter tuning was performed using gpu , but if not available code will automatically use cpu
  • Included code to load model and test it using a example image
  • The hyper - parameter tuning was performed using gpu , but if not available code will automatically use cpu
  • added tranfer learning notebooks , model , Readme.md ,improvement.md in transfer_learning directory
  • Updated main README.md

Darsh Agrawal , GSSoC 2024 extd contributor

-The current implementation utilizes EfficientNet for terrain classification.
- Data Agumentation  includes resizing, horizontal flipping, color jitter, and random rotations.
- I have loaded a pre-trained EfficientNet B0 model and modified the classifier layer to match the number of output classes (5 for different terrain types).
-  I noticed at high learning rates the accuracy was highly volatile so it was lowered
- I defined the loss function (CrossEntropyLoss) and optimizer (Adam) with a lower learning rate for fine-tuning.
- Learning rate scheduling : ReduceLROnPlateau
- Included code to load model and test it using a example image
- The hyper - parameter tuning was performed using gpu , but if not available code will automatically use cpu
-The current implementation utilizes EfficientNet for terrain classification.

Data Agumentation includes resizing, horizontal flipping, color jitter, and random rotations.
I have loaded a pre-trained EfficientNet B0 model and modified the classifier layer to match the number of output classes (5 for different terrain types).
I noticed at high learning rates the accuracy was highly volatile so it was lowered
I defined the loss function (CrossEntropyLoss) and optimizer (Adam) with a lower learning rate for fine-tuning.
Learning rate scheduling : ReduceLROnPlateau
Included code to load model and test it using a example image
The hyper - parameter tuning was performed using gpu , but if not available code will automatically use cpu
- added readme.md
-  added improvement.md
@Akasxh Akasxh merged commit 04e6178 into Akasxh:main Oct 4, 2024
@Akasxh Akasxh added documentation Improvements or additions to documentation enhancement New feature or request level3 Hard gssoc-ext Contributing to gssoc-ext hacktoberfest-accepted Contributing to hacktoberfest 24' labels Oct 4, 2024
@DarshAgrawal14
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@Akasxh it seems the corrects labels are hactoberfest , gssoc-ext , level 3 , can you please update the labels so that the associated points are awarded.

@AKSHITHA-CHILUKA
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AKSHITHA-CHILUKA commented Oct 7, 2024

@Akasxh despite of many times telling you have not changed the labels.
The crt labels are level3 with no spaces , gssoc-ext is the label not gssoc24-extd , hactoberfest-accepted is the label not hactoberfest .
I hope you will change all the label's in all the issues and pr's as soon as possible .

@Akasxh
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Akasxh commented Oct 7, 2024

@AKSHITHA-CHILUKA I am not available 24/7, I have updated the labels as soon I got back to my machine.

@AKSHITHA-CHILUKA
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so now you are available so I will see now whether you have added all the required labels or not

@Akasxh
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Akasxh commented Oct 7, 2024

I have updated all the labels please check if there's any issue.

@AKSHITHA-CHILUKA
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image
All the issues and pr should be labeled

@Akasxh
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Akasxh commented Oct 7, 2024

Should I be assigning once it's completed or just as a issue is raised?

The points are taken from prs right?

@AKSHITHA-CHILUKA
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both issue and pr if and only if both are labeled , then only the points will be reflected ,once you assign them you have to add all the labels including level labels .

@Akasxh
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Akasxh commented Oct 7, 2024

Okay done
image

@AKSHITHA-CHILUKA
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ok for further doubts please connect with @Meetjain1

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3 participants