Garbage Classification

Goal:

Garbage classification using image of the product at user level directly.






Algorithm used:

ResNet model in CNN is used here to perform image classification

Optimizers used: Adam and Adamax

Evaluation score:

0.97

Future perspective:

This system can be extended further to create single bin at public places which change internally based on the type of item being disposed capturing the image of it and changing the bin to be used accordingly. 


Advantages:

  1. It would expand the classification of products to be recycled at user level directly. It would in turn reduce the manual effort required for the same during recycling process.
  2. A user unaware of the category might mistakenly place the wrong type of items in a bin. Automating it would avoid these manual errors and reduce to effort at backend later.


Experimental observations and analysis:

Implemented the model using following ways:

  1. MobileNetV2 with sigmoid
  2. MobileNetV2 with  softmax
  3. MobileNetV2 with sigmoid
  4. ResNet with sigmoid
However, the accuracy achieved using them was low in this case.

The model finally used is:
ResNet with sigmoid
Optimizers used: Adamax and Adam

Hyperparameter tuning:  I has been done by trying different possible optimizers.


However, using Adam and Adamax together helped improving the accuracy.

Following were the patterns in Accuracy and Loss seen:



Accuracy V/S No. of Epochs for Adam optimizer


Accuracy V/S No. of Epochs comparison for Adam and Adamax optimizer



Loss V/S No. of epochs for Adam optimizer


Loss V/S No. of epochs for Adamax optimizer





Prediction of an image from Test data





Prediction of an external image taken using image capture



Code:
   



You tube video:



You tube demo:



Challenges faced: 

Handling data in form of tensors and using it to train the model.
Exploring all possible models and their hyperparameter tuning.

References:



Contributions: 

  1. Handling data variations in different formats by removing unnecessary part of content used for getting proper values.
  2. Improved the accuracy of the model to a good amount.
  3. Created an option for capturing an external image using webcam and predicting its class.

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