2050_Materials_Models

Overview

This repository, titled 2050_materials_models, is a part of the 2050-materials platform. It contains various machine learning models developed to enhance the platform's capabilities.

Models in this Repository

Currently, the repository hosts two models:

1. prediction_model_material.pth: A model for predicting the best match from a list of material types.
2. prediction_model_product.pth: A model used to predict the best match from a range of product types.

These models are built to streamline the process of identifying and categorizing different materials and products used in construction, assisting in making more environmentally informed choices.

How to Use

Requirements

The primary requirements include:

• PyTorch
• Joblib (if applicable)

Loading the Models

You can load these models using PyTorch. Here's a quick snippet on how to do it:

import torch

# Load the material prediction model
material_model_path = 'path/to/prediction_model_material.pth'
material_model = torch.load(material_model_path)

# Load the product prediction model
product_model_path = 'path/to/prediction_model_product.pth'
product_model = torch.load(product_model_path)

# Example of using the models for prediction