gnpaone / Potato-quality-classification

This repository contains the dataset and the source code for the differentiation of Potato quality from images using a CNN trained model and tensorflow

Potato-Quality-Classification

Table of Contents

• About
• Exploratory Data Analysise
• Model
• Training
• Results Evaluation
• Conclusion
• Reference

About

Potato Quality dataset is a labelled data set with 2 different quality classes. Each class contains 1000s of images. Using the data provided, a deep learning model built on TensorFlow is trained to classify into various classes in dataset.

Classes: (Defective, Non-Defective)
Epoches: 50
Batch_size: 32

Images are split to train and test set with 769 images belonging to 2 different classes.

Exploratory Data Analysis

Let's preview some of the images.

The size of the images are mostly consistent, so all the images are scaled to same size, so we dont have to worry about inconsistency.

Model

To create a convolution neural network to classfied the images, Sequential model is used.

model = models.Sequential([
    resize_and_rescale,
    data_augmentation,
    layers.Conv2D(32,(3,3),activation = 'relu',input_shape = input_shape),
    layers.MaxPooling2D((2,2)),
    layers.Conv2D(64,(3,3),activation = 'relu'),
    layers.MaxPooling2D((2,2)),
    layers.Conv2D(64,(3,3),activation = 'relu'),
    layers.MaxPooling2D((2,2)),
    layers.Conv2D(128,(3,3),activation = 'relu'),
    layers.MaxPooling2D((2,2)),
    layers.Flatten(),
    layers.Dense(256,activation = 'relu'),
    layers.Dense(n_classes,activation = 'softmax'),
])

Training

Model accuracy increased over each epoch, overfitting started at around 3 epochs. The model achieved validation accuracy of 99.34% with a 0.0397 cross entropy validation loss.

Results Evaluation

Preview some predictions from the model:

First Image to Predict:

Actual Label: Defective

Predicted Label: Defective

Now, let's examine in more detail how the model performs and evaluate those predictions.

Conclusion

With the given data sets for 2 classes of quality: Defective and Non-Defective, the model final accuracy reached 99.34% with cross entropy validation loss of 0.0397.

Reference

Potato Quality Dataset

• PepsiCo Lab Potato Chips Quality Control

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