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README.md

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-license: mit
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+# Food-101 Image Classifier
+
+This is an image classification model capable of identifying 101 different food categories from the Food-101 dataset. The model leverages transfer learning using a pre-trained EfficientNetB0 as its base.
+
+## Model Details
+
+*   **Architecture**: EfficientNetB0 (feature extractor) + Custom Dense Head
+*   **Task**: Image Classification
+*   **Dataset**: [Food-101](https://huggingface.co/datasets/ethz/food101)
+    *   101 food categories
+    *   101,000 images (750 training, 250 validation per class)
+    *   Images rescaled to a maximum side length of 512 pixels in original dataset.
+
+## Training Details
+
+### Approach
+The model was trained using **transfer learning (feature extraction)**. The pre-trained `EfficientNetB0` model, which was originally trained on the ImageNet dataset, had its layers frozen. A new custom output layer (a `GlobalAveragePooling2D` followed by a `Dense` layer with softmax activation) was added on top of the frozen EfficientNetB0 base. Only this new head was trained on the Food-101 dataset.
+
+### Preprocessing
+Images from the Food-101 dataset were preprocessed as follows:
+1.  Resized to `(256, 256)` pixels.
+2.  Pixel values cast to `tf.float32`.
+
+### Training Configuration
+*   **Optimizer**: Adam
+*   **Loss Function**: SparseCategoricalCrossentropy (suitable for integer-encoded labels)
+*   **Metrics**: Accuracy
+*   **Epochs**: 5 (with EarlyStopping if validation loss did not improve for 3 epochs)
+*   **Batch Size**: 32
+*   **Mixed Precision**: Enabled (`mixed_float16`) for faster training on compatible GPUs.
+
+### Performance
+After 5 epochs of training, the model achieved the following performance on the validation set:
+*   **Validation Loss**: 0.9174
+*   **Validation Accuracy**: 0.7482
+
+## How to Use
+
+To use this model for prediction, you'll need TensorFlow and the corresponding `EfficientNetB0` application.
+
+```python
+import tensorflow as tf
+import tensorflow_datasets as tfds
+from PIL import Image
+import numpy as np
+
+# Define the image size used during training
+IMAGE_SIZE = 256
+
+# Load the trained model
+# Make sure to replace 'path/to/your/model/effnetB0_food_model.keras' with the actual path
+loaded_model = tf.keras.models.load_model('./models/effnetB0_food_model.keras')
+
+# Get class names from the dataset info
+# (Assuming dsInfo was loaded earlier)
+# If you don't have dsInfo, you can manually create the list of class names
+class_names = [
+  "apple_pie", "baby_back_ribs", "baklava", "beef_carpaccio", "beef_tartare",
+  "beet_salad", "beignets", "bibimbap", "bread_pudding", "breakfast_burrito",
+  "bruschetta", "caesar_salad", "cannoli", "caprese_salad", "carrot_cake",
+  "ceviche", "cheesecake", "cheese_plate", "chicken_curry", "chicken_quesadilla",
+  "chicken_wings", "chocolate_cake", "chocolate_mousse", "churros", "clam_chowder",
+  "club_sandwich", "crab_cakes", "creme_brulee", "croque_madame", "cup_cakes",
+  "deviled_eggs", "donuts", "dumplings", "edamame", "eggs_benedict",
+  "escargots", "falafel", "filet_mignon", "fish_and_chips", "foie_gras",
+  "french_fries", "french_onion_soup", "french_toast", "fried_calamari", "fried_rice",
+  "frozen_yogurt", "garlic_bread", "gnocchi", "greek_salad", "grilled_cheese_sandwich",
+  "grilled_salmon", "guacamole", "gyoza", "hamburger", "hot_and_sour_soup",
+  "hot_dog", "huevos_rancheros", "hummus", "ice_cream", "lasagna",
+  "lobster_bisque", "lobster_roll_sandwich", "macaroni_and_cheese", "macarons", "miso_soup",
+  "mussels", "nachos", "omelette", "onion_rings", "oysters",
+  "pad_thai", "paella", "pancakes", "panna_cotta", "peking_duck",
+  "pho", "pizza", "pork_chop", "poutine", "prime_rib",
+  "pulled_pork_sandwich", "ramen", "ravioli", "red_velvet_cake", "risotto",
+  "samosa", "sashimi", "scallops", "seaweed_salad", "shrimp_and_grits",
+  "spaghetti_bolognese", "spaghetti_carbonara", "spring_rolls", "steak", "strawberry_shortcake",
+  "sushi", "tacos", "takoyaki", "tiramisu", "tuna_tartare", "waffles"
+]
+
+def preprocess_image(image_path):
+    img = tf.io.read_file(image_path)
+    img = tf.image.decode_jpeg(img, channels=3)
+    img = tf.image.resize(img, [IMAGE_SIZE, IMAGE_SIZE])
+    img = tf.cast(img, tf.float32) # Already normalized implicitly by EfficientNet's internal preprocessing
+    img = tf.expand_dims(img, axis=0) # Add batch dimension
+    return img
+
+# Example usage with a dummy image path (replace with your actual image)
+# You might need to download a sample food image for testing
+# For example, from the Food-101 dataset itself or any food image.
+# dummy_image_path = tf.keras.utils.get_file('pizza.jpg', 'https://upload.wikimedia.org/wikipedia/commons/thumb/a/a3/Eq_pizza_italy_vs_us.jpg/640px-Eq_pizza_italy_vs_us.jpg')
+
+# Preprocess the image
+# preprocessed_image = preprocess_image(dummy_image_path)
+
+# Make a prediction
+# predictions = loaded_model.predict(preprocessed_image)
+# predicted_class_index = np.argmax(predictions[0])
+# predicted_class_name = class_names[predicted_class_index]
+
+# print(f"The predicted food item is: {predicted_class_name}")
+# print(f"Prediction probabilities: {predictions[0][predicted_class_index]:.4f}")
+
+
+```
+
+## License
+
+[MIT]