Vegetables Classification
Dataset: https://www.kaggle.com/datasets/misrakahmed/vegetable-image-dataset
Workflow
The example follows the same visual workflow supported by Neural Network Designer: load the dataset, design the neural network, train the model and evaluate its results.
Dataset
Load vegetable images from folders and prepare the image dataset for classification.
Network Design
Visually design the neural network by adding compatible deep-learning layers.
Training
Train the designed network and monitor the learning process.
Evaluation
Evaluate the trained classifier and analyze the classification results.
Vegetables Classification in Action
The following four screenshots illustrate the complete classification workflow inside Neural Network Designer.
📊 Vegetable Dataset
The vegetable image dataset is loaded into Neural Network Designer. The image-data interface provides the starting point for preparing the classification problem.
🎛️ Neural Network Design
The neural network architecture is designed visually using the Neural Network Designer canvas and compatible deep-learning layers.
⚡ Model Training
The designed neural network is trained using the vegetable image dataset while the training process is monitored through the Neural Network Designer interface.
📈 Classification Evaluation ( Accuracy: 96% )
The trained classification model is evaluated using the evaluation interface to inspect its classification performance.
Why Use Neural Network Designer?
Visual Design
Design neural networks using a visual drag-and-drop workflow.
Image Data
Work with image-folder datasets directly through the data management interface.
White-Box Errors
Identify layer compatibility problems and understand why a network configuration fails.
Evaluation
Inspect model performance through the evaluation workflow.
Build Your Own Image Classifier
Design, train and evaluate neural networks visually with Neural Network Designer.
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