
# RecycleTree - Others Classification Model
![Banner](https://huggingface.co/pyesonekyaw/recycletree_plastic/resolve/main/banner.png)

RecycleTree is a project from CZ3002 Advanced Software Engineering in Nanyang Technological University. It aims to enable users to have a more informed recycling experience, from finding the nearest recycling bins, to checking whether the item they wish to recycle can indeed be recycled, to learning more about recycling and contamination in general.

The whole project can be found on [GitHub](https://github.com/py-sk/RecycleTree)

This image classification model in particular is to classify plastic trash items into the following classes:
* Battery
* Electronic Waste
* Stationery
  
## Training Data
The training dataset had 11453 images across 3 classes, with each class having roughly the same distribution of images. The images were either scraped from Google image search, or obtained by ourselves in real life. 

## Training Procedure
As the purpose of this model was to act just as a proof of concept for quick prototyping of RecycleTree, I opted to use the fast.ai library and a simple model architecture of ResNet34.
The training procedure is following the recommendations from [fast.ai](https://docs.fast.ai/)

## Other Models
There are also other models in the RecycleTree model series:
* [Materials Classification Model](https://huggingface.co/pyesonekyaw/recycletree_materials) - Classification of images of trash into different materials
* [Paper Classification Model](https://huggingface.co/pyesonekyaw/recycletree_paper) - Classification of images of paper trash into different classes
* [Metal Classification Model](https://huggingface.co/pyesonekyaw/recycletree_metal) - Classification of images of metal trash into different classes
* [Glass Classification Model](https://huggingface.co/pyesonekyaw/recycletree_glass) - Classification of images of glass trash into different classes
* [Plastic Classification Model](https://huggingface.co/pyesonekyaw/recycletree_plastic) - Classification of images of plastic trash into different classes
{"license": "openrail", "library_name": "fastai", "tags": ["fastai"], "pipeline_tag": "image-classification"}
fastai image-classification license:openrail has_space region:us
TAGS
#fastai #image-classification #license-openrail #has_space #region-us 


# RecycleTree - Others Classification Model
!Banner

RecycleTree is a project from CZ3002 Advanced Software Engineering in Nanyang Technological University. It aims to enable users to have a more informed recycling experience, from finding the nearest recycling bins, to checking whether the item they wish to recycle can indeed be recycled, to learning more about recycling and contamination in general.

The whole project can be found on GitHub

This image classification model in particular is to classify plastic trash items into the following classes:
* Battery
* Electronic Waste
* Stationery
  
## Training Data
The training dataset had 11453 images across 3 classes, with each class having roughly the same distribution of images. The images were either scraped from Google image search, or obtained by ourselves in real life. 

## Training Procedure
As the purpose of this model was to act just as a proof of concept for quick prototyping of RecycleTree, I opted to use the URL library and a simple model architecture of ResNet34.
The training procedure is following the recommendations from URL

## Other Models
There are also other models in the RecycleTree model series:
* Materials Classification Model - Classification of images of trash into different materials
* Paper Classification Model - Classification of images of paper trash into different classes
* Metal Classification Model - Classification of images of metal trash into different classes
* Glass Classification Model - Classification of images of glass trash into different classes
* Plastic Classification Model - Classification of images of plastic trash into different classes
# RecycleTree - Others Classification Model
!Banner

RecycleTree is a project from CZ3002 Advanced Software Engineering in Nanyang Technological University. It aims to enable users to have a more informed recycling experience, from finding the nearest recycling bins, to checking whether the item they wish to recycle can indeed be recycled, to learning more about recycling and contamination in general.

The whole project can be found on GitHub

This image classification model in particular is to classify plastic trash items into the following classes:
* Battery
* Electronic Waste
* Stationery ## Training Data
The training dataset had 11453 images across 3 classes, with each class having roughly the same distribution of images. The images were either scraped from Google image search, or obtained by ourselves in real life. ## Training Procedure
As the purpose of this model was to act just as a proof of concept for quick prototyping of RecycleTree, I opted to use the URL library and a simple model architecture of ResNet34.
The training procedure is following the recommendations from URL ## Other Models
There are also other models in the RecycleTree model series:
* Materials Classification Model - Classification of images of trash into different materials
* Paper Classification Model - Classification of images of paper trash into different classes
* Metal Classification Model - Classification of images of metal trash into different classes
* Glass Classification Model - Classification of images of glass trash into different classes
* Plastic Classification Model - Classification of images of plastic trash into different classes
TAGS
#fastai #image-classification #license-openrail #has_space #region-us 
 # RecycleTree - Others Classification Model
!Banner

RecycleTree is a project from CZ3002 Advanced Software Engineering in Nanyang Technological University. It aims to enable users to have a more informed recycling experience, from finding the nearest recycling bins, to checking whether the item they wish to recycle can indeed be recycled, to learning more about recycling and contamination in general.

The whole project can be found on GitHub

This image classification model in particular is to classify plastic trash items into the following classes:
* Battery
* Electronic Waste
* Stationery ## Training Data
The training dataset had 11453 images across 3 classes, with each class having roughly the same distribution of images. The images were either scraped from Google image search, or obtained by ourselves in real life. ## Training Procedure
As the purpose of this model was to act just as a proof of concept for quick prototyping of RecycleTree, I opted to use the URL library and a simple model architecture of ResNet34.
The training procedure is following the recommendations from URL ## Other Models
There are also other models in the RecycleTree model series:
* Materials Classification Model - Classification of images of trash into different materials
* Paper Classification Model - Classification of images of paper trash into different classes
* Metal Classification Model - Classification of images of metal trash into different classes
* Glass Classification Model - Classification of images of glass trash into different classes
* Plastic Classification Model - Classification of images of plastic trash into different classes
TAGS
#fastai #image-classification #license-openrail #has_space #region-us 
# RecycleTree - Others Classification Model
!Banner

RecycleTree is a project from CZ3002 Advanced Software Engineering in Nanyang Technological University. It aims to enable users to have a more informed recycling experience, from finding the nearest recycling bins, to checking whether the item they wish to recycle can indeed be recycled, to learning more about recycling and contamination in general.

The whole project can be found on GitHub

This image classification model in particular is to classify plastic trash items into the following classes:
* Battery
* Electronic Waste
* Stationery## Training Data
The training dataset had 11453 images across 3 classes, with each class having roughly the same distribution of images. The images were either scraped from Google image search, or obtained by ourselves in real life.## Training Procedure
As the purpose of this model was to act just as a proof of concept for quick prototyping of RecycleTree, I opted to use the URL library and a simple model architecture of ResNet34.
The training procedure is following the recommendations from URL## Other Models
There are also other models in the RecycleTree model series:
* Materials Classification Model - Classification of images of trash into different materials
* Paper Classification Model - Classification of images of paper trash into different classes
* Metal Classification Model - Classification of images of metal trash into different classes
* Glass Classification Model - Classification of images of glass trash into different classes
* Plastic Classification Model - Classification of images of plastic trash into different classes