VGG16 Fruits Classifier

Model Description

This model is a fine-tuned version of the VGG16 architecture on the Fruits-360 dataset from Kaggle. VGG16 is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford. The model achieves a high level of accuracy in recognizing 131 different types of fruits.

Intended Uses & Limitations

Intended Uses

Food Recognition: This model can be used to classify images of fruits into one of 131 categories. It is particularly useful for applications in grocery stores, dietary monitoring, and educational tools about fruits.

Limitations

Generalization: The model is trained specifically on the Fruits-360 dataset and may not generalize well to fruits not included in this dataset or images with significantly different characteristics (e.g., different lighting, angles, or backgrounds).
Biases: Performance may vary across different fruit categories due to the distribution and quality of the dataset images.

Training and Evaluation Data

The model was trained and evaluated on the Fruits-360 dataset. This dataset contains over 70,000 images of fruits, split into training and test sets.

Dataset Statistics

Training Set: 54,190 images
Validation Set: 13,502 images
Test Set: 22,688 images

Each image is labeled with one of 131 fruit categories.

Training Procedure

Data Augmentation

Preprocessing: All images were preprocessed using the preprocess_input function from the VGG16 module.
Augmentation: Images were augmented with random rotations, shifts, zooms, flips, and more to improve the model's generalization.

Model Architecture

The model architecture consists of the VGG16 base with the top fully connected layers removed. A global average pooling layer and a dense output layer with softmax activation were added.

Hyperparameters

Optimizer: Adam with a learning rate of 0.0001
Loss Function: Categorical Crossentropy
Batch Size: 64
Epochs: 5

Training Results

The model was trained for 5 epochs, achieving a validation accuracy of 97.04% and a test accuracy of 96.02%.

Evaluation Metrics

Accuracy: 96.02%
Loss: 0.1585

These metrics indicate that the model performs very well on the test set of the Fruits-360 dataset.

Framework Versions

TensorFlow: 2.x
Keras: 2.x

Example Code

You can use this model with the following example code:

from tensorflow.keras.preprocessing import image
from tensorflow.keras.applications.vgg16 import preprocess_input
import numpy as np

# Load the model
model = tf.keras.models.load_model('path_to_model')

# Load an image file that you want to classify
img_path = 'path_to_image'
img = image.load_img(img_path, target_size=(100, 100))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

# Predict the class of the image
predictions = model.predict(x)
predicted_class = np.argmax(predictions, axis=1)
print(f"Predicted class: {predicted_class}")