Update README.md

README.md

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 ---
 tags:
 - fastai
+library_name: fastai
+pipeline_tag: image-classification
+license: openrail
 ---
 
-# Amazing!
-
-🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
-
-# Some next steps
-1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
-
-2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
-
-3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
-
-Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
-
-
----
-
-
-# Model card
-
-## Model description
-More information needed
-
-## Intended uses & limitations
-More information needed
-
-## Training and evaluation data
-More information needed
+# RecycleTree - Metal 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:
+aerosol_can', 'aluminum_tray_foil', 'metal_can_or_container
+* Aerosol Can
+* Aluminum Tray Foil
+* Metal Can/Container
+  
+## Training Data
+The training dataset had 10872 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
+* [Plastic Classification Model](https://huggingface.co/pyesonekyaw/recycletree_plastic) - Classification of images of plastic trash into different classes
+* [Glass Classification Model](https://huggingface.co/pyesonekyaw/recycletree_glass) - Classification of images of glass trash into different classes
+* [Others Classification Model](https://huggingface.co/pyesonekyaw/recycletree_others) - Classification of images of other (not paper, metal, glass, or plastic) trash into different classes