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Image Classification with Deep Learning


Image Classification Using Deep Learning


When creating autonomous systems, it is important for them to understand their environment and the objects they encounter. For example, let's consider a robotic arm designed to sort trash and recycling items. To enable the robot to distinguish between different objects, we need to perform image classification. Deep learning is a highly accurate method for classifying images, where we train a neural network to recognize patterns and features in images to make accurate predictions.

What is Deep Learning?

Deep learning is a process that involves teaching a machine to learn from experience, similar to how humans learn. It uses a neural network and large amounts of data to train the machine to recognize patterns and features. In some cases, the machine can even surpass human-level performance. In the context of image classification, deep learning helps us create a neural network that takes an image as input and predicts the class or type of object in that image.

Processing Image Data

To demonstrate the process of image classification, let's take a look at an example. In this example, we have collected color images of bottles, cans, and detergent pouches. Each object has hundreds of photos taken at different angles. We'll use MATLAB to process the image data and train a neural network for accurate classification.

Creating a Data Store

When preparing data for deep learning, we start by creating a data store in MATLAB. A data store allows us to store and organize our data for training and testing purposes. In this case, we'll use an image data store to store our images. We specify the location of the data and set the label source to the folder names to assign class labels to the images. Additionally, we split our data into training, validation, and testing sets to assess the performance of the network.

Training the Neural Network

Now that our data is organized, we can proceed to train the neural network. There are two main approaches to creating a deep neural network: starting from scratch or using transfer learning. Transfer learning is a preferred option when the dataset is small. In this example, we'll use transfer learning by starting with a pre-trained network called ResNet-18.

Modifying the Pre-trained Network

We make a few modifications to the pre-trained network to adapt it to our specific classification problem. We adjust the output size of the fully connected layer to match the number of classes in our dataset (three in this case). These modifications allow the network to learn to classify the objects correctly.

Augmentation for Robustness

To ensure that our network can classify objects correctly in various situations, we use augmentation techniques. Augmentation involves applying random effects to the images during training to introduce variation. By doing so, the network learns to recognize objects under different conditions. We can adjust the amount of variation introduced by setting minimum and maximum values for each effect.

Evaluating the Model

After training the network, we evaluate its performance on new data. We can use the classify function to predict the class of a single image or an entire data store. By comparing the predicted class names with the expected class names, we can assess the accuracy of the model. Additionally, the predict function provides a measure of confidence for each prediction.


In conclusion, image classification using deep learning is a powerful technique for training machines to recognize objects in images. By creating a data store, training a neural network, and evaluating its performance, we can develop accurate and robust models for various classification tasks. The use of transfer learning and data augmentation techniques further enhances the effectiveness of the models.

If you have any questions while working through this process or the included exercise, please feel free to reach out to us at Robotics Arena at MathWorks.

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