Fruit Classification

Fruit Classification Model that demonstrate how to perform fruit classification. The model is trained to classify different types of fruits from images.

Note

I ran the code on Google Colab and forgot to create the requirements.txt file. Please install the necessary dependencies manually.

Dataset

The dataset used for training and testing is the Fruits-360 dataset, which contains images of various fruits.

Dataset properties

• The total number of images: 90483.

• Training set size: 67692 images (one object per image).

• Test set size: 22688 images (one object per image).

• The number of classes: 131 (fruits and vegetables).

• Image size: 100x100 pixels.

Model Structure

used vgg16 model structure and we added the last 2 layers to it in order to get the advantage of vgg16 feature extraction.

Model: "model"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 input_1 (InputLayer)        [(None, 100, 100, 3)]     0         
                                                                 
 block1_conv1 (Conv2D)       (None, 100, 100, 64)      1792      
                                                                 
 block1_conv2 (Conv2D)       (None, 100, 100, 64)      36928     
                                                                 
 block1_pool (MaxPooling2D)  (None, 50, 50, 64)        0         
                                                                 
 block2_conv1 (Conv2D)       (None, 50, 50, 128)       73856     
                                                                 
 block2_conv2 (Conv2D)       (None, 50, 50, 128)       147584    
                                                                 
 block2_pool (MaxPooling2D)  (None, 25, 25, 128)       0         
                                                                 
 block3_conv1 (Conv2D)       (None, 25, 25, 256)       295168    
                                                                 
 block3_conv2 (Conv2D)       (None, 25, 25, 256)       590080    
                                                                 
 block3_conv3 (Conv2D)       (None, 25, 25, 256)       590080    
                                                                 
 block3_pool (MaxPooling2D)  (None, 12, 12, 256)       0         
                                                                 
 block4_conv1 (Conv2D)       (None, 12, 12, 512)       1180160   
                                                                 
 block4_conv2 (Conv2D)       (None, 12, 12, 512)       2359808   
                                                                 
 block4_conv3 (Conv2D)       (None, 12, 12, 512)       2359808   
                                                                 
 block4_pool (MaxPooling2D)  (None, 6, 6, 512)         0         
                                                                 
 block5_conv1 (Conv2D)       (None, 6, 6, 512)         2359808   
                                                                 
 block5_conv2 (Conv2D)       (None, 6, 6, 512)         2359808   
                                                                 
 block5_conv3 (Conv2D)       (None, 6, 6, 512)         2359808   
                                                                 
 block5_pool (MaxPooling2D)  (None, 3, 3, 512)         0         
                                                                 
 flatten (Flatten)           (None, 4608)              0         
                                                                 
 dense (Dense)               (None, 24)                110616

Result

the experiment was done on google colab.

training

• Epochs: 20

• Training Loss: 3%, Validation Loss: 2%.

• Training Accuracy: 99%, Validation Accuracy: 99%

Prerequisites

• Python 3.x

• Required libraries: numpy, keras, tensorflow, scipy, matplotlib

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests or for any improvement.

Contact

For any inquiries or support, please contact Adnan AlKharfan.