Honey Bee Pollen Detection

[RAJharsh02]

Pull Request for DL-Simplified 💡

Issue Title : Honey Bee Pollen Detection #346

• Info about the related issue (Aim of the project) : To classify if a given image of honey bee contains pollen or not.
• Name: Harsh Raj
• GitHub ID: https://github.com/RAJharsh02
• Email ID: https://github.com/RAJharsh02
  Email ID : [email protected]
• Idenitfy yourself: (Mention in which program you are contributing in. Eg. For a JWOC 2022 participant it's, JWOC Participant) SSOC23

Closes: #346

Describe the add-ons or changes you've made 📃

Implemented a CNN, DenseNet and VGG16 for image classification to predict whether the given image of honey bee contains pollen or not.
During the honeybee image pollen detection project, several add-ons were incorporated to enhance the overall performance and usability of the model.
Firstly, data augmentation techniques were applied to increase the dataset's diversity, enabling the model to handle variations in image conditions effectively.
Secondly, transfer learning was leveraged using pre-trained VGG16 and DenseNet architectures to expedite training and improve accuracy. Additionally, a post-processing step was implemented, applying a threshold to the model's output probabilities for confident binary predictions.
Updated the README file with detailed instructions and documentation.

Type of change ☑️

What sort of change have you made:

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Code style update (formatting, local variables)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• This change requires a documentation update

How Has This Been Tested? ⚙️

To keep a clean code and concentrate on one model at a time, I decided to take the approach of creating separate ipynb files for each models. Aside from the model architecture, all models' code adheres to the same process for all other aspects.

The evaluation metrics I used to assess the models:
Accuracy
Loss

Checklist: ☑️

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

[github-actions]

Our team will soon review your PR. Thanks @RAJharsh02 :)

[abhisheks008]

Everything looks pretty good to me, but you need to change the project folder name from Honey-Bee-Pollen-detection to Honey Bee Pollen Detection, no hyphen ('-') should be there in the folder name.

@RAJharsh02

[RAJharsh02]

@abhisheks008 I have made the required changes. please look at it

[abhisheks008]

Your PR is approved and ready to be merged.
@RAJharsh02