Fruit Classification Model that demonstrate how to perform fruit classification. The model is trained to classify different types of fruits from images.
I ran the code on Google Colab and forgot to create the requirements.txt file. Please install the necessary dependencies manually.
The dataset used for training and testing is the Fruits-360 dataset, which contains images of various fruits.
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The total number of images: 90483.
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Training set size: 67692 images (one object per image).
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Test set size: 22688 images (one object per image).
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The number of classes: 131 (fruits and vegetables).
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Image size: 100x100 pixels.
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"
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Layer (type) Output Shape Param #
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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
the experiment was done on google colab.
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Epochs: 20
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Training Loss: 3%, Validation Loss: 2%.
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Training Accuracy: 99%, Validation Accuracy: 99%
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Python 3.x
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Required libraries: numpy, keras, tensorflow, scipy, matplotlib
Contributions are welcome! Feel free to open issues or submit pull requests or for any improvement.
For any inquiries or support, please contact Adnan AlKharfan.