Model VGG

This function block integrates a VGG convolutional neural network model into your project, allowing you to perform image classification tasks using VGG16 or VGG19 architectures.

📥 Inputs

Choose Folder 2D The input expects data from a "Choose Folder 2D" block, which should provide images in a compatible format for model evaluation.

📤 Outputs

This function block outputs a trained VGG model ready for evaluation and inference.

🕹️ Controls

Model Type A dropdown menu to select either the VGG16 or VGG19 model architecture.

Input Size A field to specify the input size for the model, representing the dimensions of the images (must be at least 32).

Pooling A dropdown to select the pooling method to be used in the model (Max, Average, or None).

🎨 Features

Pre-Trained Models Users can select from two popular VGG architectures designed for image classification tasks.

Flexible Input Size The input size can be adjusted according to your image dimensions for compatibility with the VGG architecture.

Pooling Options Offers choices between different pooling methods, enabling customized model architecture.

📝 Usage Instructions

1. Input Connection: Connect the output from a "Choose Folder 2D" block to the input of this function block.

2. Select Model: Choose either VGG16 or VGG19 from the Model Type dropdown.

3. Input Size: Specify the desired input size for the images in the Input Size field. Make sure the value is 32 or greater.

4. Choose Pooling Method: Select a pooling method (Max, Average, or None) from the Pooling dropdown.

5. Evaluate: Run the function block to build and return the selected VGG model, which will then be ready for evaluating images.

📊 Evaluation

When executed, this function block creates and returns a VGG model that takes images of the specified size and applies the selected pooling method, ready for inference.

💡 Tips and Tricks

Choosing Input Size

For optimal results, consider using standard input sizes like 64, 128, or 224 depending on your dataset and model choice. Models like VGG are often trained with 224x224 images.

Pooling Methods

Test different pooling methods to see how they impact the performance of the neural network. Average pooling can help reduce dimensionality effectively, but max pooling might preserve features better.

Model Type Consideration

Both VGG16 and VGG19 hold similar functionality, but VGG19 has more layers and can capture subtle visual features better. Depending on your use case, it may yield different classification results.

🛠️ Troubleshooting

Invalid Input Size

Ensure that the input size specified is a numeric value and not less than 32. This input is critical for the model to process incoming images correctly.

RGB Color Type Requirement

If you encounter an error regarding input color type, ensure that your images are in RGB format, as the VGG model is specifically trained to recognize such color input.