Food Item Identification Using Python

This repository contains a Python project for identifying food items in images using a pretrained model from Hugging Face. The model, nateraw/food, leverages advanced machine learning techniques to classify various food items with high accuracy.

Features

• Pretrained Model: Utilizes the nateraw/food model from Hugging Face for image classification.
• Image Processing: Handles image preprocessing using the transformers and PIL libraries.
• Batch Processing: Supports processing multiple images from a directory.
• User-Friendly: Easy-to-understand code with clear instructions.

Getting Started

1. Clone the repository:

   git clone https://github.com/your-username/Food-Item-Identification-Using-Python.git

2. Install the required packages:

   pip install -r requirements.txt

3. Run the script:
   • Place your images in the Images directory.
   • Execute the script to identify food items in your images.

Requirements

• Python 3.x
• transformers
• torch
• Pillow

Usage

Here is an example of how to use the code:

Step 1: Load the Model and Tokenizer

from transformers import AutoModelForImageClassification, AutoImageProcessor
from PIL import Image
import torch

# Load the model
model_name = "nateraw/food"
model = AutoModelForImageClassification.from_pretrained(model_name)
processor = AutoImageProcessor.from_pretrained(model_name)