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FreshMeat Classifier
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  • Fresh Meat Classifier using fastai and PyTorch

    This notebook demonstrates the process of building a Fresh Meat classifier using the fastai library, which is built on top of PyTorch.

    Fastai is a high-level library that simplifies the process of training deep learning models. It provides easy-to-use APIs and pre-built architectures, making it accessible for both beginners and experienced practitioners.

    In this notebook, we will explore the steps involved in training a Fresh Meat classifier using fastai and PyTorch. We will cover data preprocessing, model creation, training, and evaluation.

    !pip install fastai
    Hidden output

    Extracting from 'Freshness'

    import zipfile
    import os
    def extract_zip_file(zip_file_path, destination_folder, extracted_folder_name):
        # Extract the zip file
        with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
        # Specify the paths of the train and valid folders
        extracted_folder_path = os.path.join(destination_folder, extracted_folder_name)
        train_folder_path = os.path.join(extracted_folder_path, 'train')
        valid_folder_path = os.path.join(extracted_folder_path, 'valid')
        # Check if the train and valid folders exist
        if os.path.exists(train_folder_path) and os.path.exists(valid_folder_path):
            return "Extraction successful."
            return "Extraction failed."
    # Specify the path to the zip file
    zip_file_path = 'Freshness'
    # Specify the destination folder to extract the files
    destination_folder = '.'
    # Specify the name of the extracted folder
    extracted_folder_name = 'Freshness Dataset'
    # Call the function
    result = extract_zip_file(zip_file_path, destination_folder, extracted_folder_name)
    Hidden output
    !mv Meat\ Freshness.v1-new-dataset.multiclass FreshnessDataSet

    Checking the lengths of train and valid folders

    import os
    # Specify the paths of the train and valid folders
    train_folder_path = os.path.join('FreshnessDataSet', 'train')
    valid_folder_path = os.path.join('FreshnessDataSet', 'valid')
    # Get the number of files in the train folder
    train_files = os.listdir(train_folder_path)
    num_train_files = len(train_files)
    # Get the number of files in the valid folder
    valid_files = os.listdir(valid_folder_path)
    num_valid_files = len(valid_files)
    num_train_files, num_valid_files
    !ls /work/files/workspace/FreshnessDataSet/
    import os
    # List the contents of the train directory
    print("Train Directory Contents:")
    # List the contents of the valid directory
    print("\nValid Directory Contents:")
    Hidden output

    Need to create subfolders Fresh/Half-Fresh/Spoiled in Train and Valid folders

    import os
    import shutil
    base_dir = 'FreshnessDataSet'
    classes = ['FRESH', 'HALF-FRESH', 'SPOILED']
    # Function to create subdirectories and move files
    def organize_dataset(folder_name):
        folder_path = os.path.join(base_dir, folder_name)
        if not os.path.exists(folder_path):
            print(f"Folder {folder_path} not found.")
        # Create class subdirectories
        for cls in classes:
            cls_dir = os.path.join(folder_path, cls)
            os.makedirs(cls_dir, exist_ok=True)
        # Move files to their respective class subdirectories
        for file in os.listdir(folder_path):
            if file.endswith('.jpg'):
                for cls in classes:
                    # Check if the class name is at the beginning of the filename
                    if file.startswith(cls):
                        shutil.move(os.path.join(folder_path, file), os.path.join(folder_path, cls, file))
    # Organize train and valid directories
    print("Dataset organization complete.")

    Loading the Dataset and Fine-tuning a CNN Model

    To load the dataset into dls and fine-tune a CNN model resnet18 for five iterations, you can use the following code:

    from import *
    data_path = 'FreshnessDataSet'
    # Load the dataset
    dls = ImageDataLoaders.from_folder(
        item_tfms=Resize(196),  # Ensure images are resized to 416x416
        batch_tfms=aug_transforms(max_zoom=1.2, max_lighting=0.2),  # Optional
        bs=16,  # Reduced batch size
    # Create a CNN model
    learn = cnn_learner(dls, resnet18, metrics=accuracy)
    # Train the model
    learn.fine_tune(10)  # Number of epochs, adjust as needed
    # Generate confusion matrix from learned model
    interp = ClassificationInterpretation.from_learner(learn)
    # Evaluate the model's performance