Food Item Classification (pranjay-poddar#1615) (pranjay-poddar#2065)

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This file contains the google drive link for the dataset.

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ML Projects/Food Item Classification/Dataset.md

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+# Dataset 
+
+### The link for the dataset is - https://drive.google.com/drive/folders/1Tq3wHQxtudn3IO4CSym9NRp2r1vdwnG7?usp=sharing
+

ML Projects/Food Item Classification/README.md

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+# Food Item Classification
+
+### 1. Importing the required libraries:
+   - TensorFlow and its Keras module for building and training neural networks.
+   - Matplotlib for visualizing images and plots.
+   - IPython.display for displaying HTML content.
+
+### 2. Defining constants:
+   - BATCH_SIZE represents the number of images to be processed in each batch during training.
+   - IMAGE_SIZE defines the desired size for input images.
+   - CHANNELS specifies the number of color channels in the images (RGB has 3 channels).
+   - EPOCHS represents the number of training epochs (iterations).
+
+### 3. Loading the dataset:
+   - tf.keras.preprocessing.image_dataset_from_directory() is used to load images from a directory.
+   - The function takes parameters such as directory path, seed for shuffling, image size, and batch size.
+   - The dataset is stored in the dataset variable.
+
+### 4. Retrieving class names:
+   - The class_names variable is assigned the class names extracted from the dataset.
+
+### 5. Displaying sample images:
+   - The code displays a grid of 12 images from the dataset along with their corresponding labels.
+   - This is done using Matplotlib's plt.imshow() and plt.title() functions.
+
+### 6. Partitioning the dataset:
+   - The dataset is split into training, validation, and test sets using the get_dataset_partitions_tf() function.
+   - The function takes the dataset and the desired split percentages as parameters.
+   - The training set is assigned to train_ds, the validation set to val_ds, and the test set to test_ds.
+
+### 7. Preparing the datasets for training:
+   - The training, validation, and test datasets are cached, shuffled, and prefetched to optimize data loading.
+   - tf. data. Dataset methods like cache(), shuffle(), and prefetch() are used.
+
+### 8. Data augmentation:
+   - Data augmentation is performed to increase the dataset's diversity and improve model generalization.
+   - The data_augmentation sequence applies random flips and rotations to the images.
+
+### 9. Model architecture:
+   - The model is built using the Sequential API of Keras.
+   - It consists of a series of convolutional (Conv2D) and max-pooling (MaxPooling2D) layers.
+   - The convolutional layers extract features from the input images.
+   - The final layers are a flattened layer, a dense layer with ReLU activation, and a dense layer with softmax activation for multi-class classification.
+
+### 10. Model compilation:
+   - The model is compiled with the Adam optimizer, sparse categorical cross-entropy loss, and accuracy metric.
+
+### 11. Model training:
+   - The fit() function is called to train the model on the training data.
+   - Parameters include the training data, batch size, validation data, verbosity, and the number of epochs.
+   - The training history is stored in the history variable.
+
+### 12. Model evaluation:
+   - The evaluate() function is used to evaluate the model's performance on the test dataset.
+   - The evaluation results (loss and accuracy) are stored in the scores variable.
+
+### 13. Predicting single images:
+   - A function predict() is defined to predict the class of a single image.
+   - It takes the model and an image as input, preprocesses the image, and returns the predicted class and confidence.
+
+### 14. Visualizing predictions:
+   - The code selects a batch of images from the test dataset and visualizes their actual and predicted classes using the predict() function.
+
+### 15. Saving the model:
+   - The trained model is saved to a directory using the save() function.
+
+### 16. Installing Gradio:
+   - Gradio is installed using the pip install command.
+
+### 17. Creating a Gradio interface:
+   - A Gradio interface is created to allow interactive image classification using the trained model.
+   - The predict_image() function is defined to preprocess and predict the class probabilities for an input image.
+   - The interface specifies an input image and a label as outputs, and it launches the interface when executed.