Model ResNet
This function block allows users to utilize various ResNet models for image classification tasks. It provides different options for model selection and input configurations.
π₯ Inputs
Choose Folder 2D
Connect to an input block that provides image data ready for processing through the ResNet model.
π€ Outputs
The output of this function block is the corresponding model ready to be evaluated on input images.
πΉοΈ Controls
Model Type
A dropdown menu that allows users to select the ResNet variant to use (e.g., ResNet50, ResNet101, etc.).
Input Size
A text input to specify the desired input image size for the model. It should be an integer value that meets the minimum requirement (32 pixels).
Pooling
A dropdown menu to select the pooling type to be used in the model (Maximum, Average, or None).
π¨ Features
Multiple Model Options
Offers a variety of ResNet architectures to choose from, catering to different needs based on performance and complexity.
Dynamic Input Size
User-defined input size allows flexibility based on the input images being used.
Pooling Options
Users can decide on the pooling layer to be used in the architecture, which can affect the model's classification performance.
π Usage Instructions
Connect Input: Link this block to an appropriate input block that provides images for processing, such as a folder of images.
Select Model Type: Choose the desired ResNet model variant from the
Model Type
dropdown.Specify Input Size: Enter an integer value in the
Input Size
field (minimum size is 32).Select Pooling Method: Choose a pooling method from the
Pooling
dropdown to define how the pooling layers will be handled.Evaluate Model: Run the block, and it will output the ResNet model configured with specified parameters for classification tasks.
π Evaluation
Upon evaluating the block, it will return a configured ResNet model prepared for image classification based on the connected image input.
π‘ Tips and Tricks
π οΈ Troubleshooting
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