Update README.md

README.md

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 This model is an image classification model trained to identify different types of food from images. It was developed as part of a Food Vision project, likely utilizing transfer learning on a pre-trained convolutional neural network.
 
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 ## Model Details
 
 ### Model Description
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 * Organizing food images in databases.
 
 * Assisting in dietary tracking or recipe suggestions based on images.
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 ## Limitations
 
 * **Dataset Bias:** The model is trained on the Food101 dataset. Its performance may degrade on food images that are significantly different in style, presentation, or origin from those in the training data.
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 Transfer learning helped the model achieve greater accuracy, though the model struggled with food closely related to each other indicating more data was needed. The Dataset used alot but more data is still needed to differentiate between closely looking food.
 
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 ## Environmental Impact
 
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 * **Carbon Emitted:** 80 grams of CO2eq (estimated)
 
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 ## Technical Specifications
 
 ### Model Architecture and Objective
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 * Helper functions from `helper_functions.py` (likely for plotting, data handling)
 
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 ## Usage
 
 Here's an example of how to use the model for inference on a new image. This assumes the model has been saved in a TensorFlow SavedModel format.