Icon

Image Classification Application

This workflow reads images of cats and dogs, performs some simple preprocessing, and trains and evaluates a Convolutional Neural Network (CNN) that is able to distinguish cat images from dog images.

Reference:
F. Villaroel Ordenes & R. Silipo, “Machine learning for marketing on the KNIME Hub: The development of a live repository for marketing applications”, Journal of Business Research 137(1):393-410, DOI: 10.1016/j.jbusres.2021.08.036.


The Conda Environment Propagation node ensures theexistence of a Conda environment with all packages.Another option is to setup your Python integration to usea Conda environment with all packages as describedhere: https://docs.knime.com/2021-12/deep_learning_installation_guide/index.html#dl_python_setup Read and preprocess images Train and evaluate the CNN Define CNN architecture Find the data here!https://www.kaggle.com/c/dogs-vs-cats/data Image Classification ApplicationThis workflow reads images of cats and dogs, performs some image preprocessing, and trains and evaluates a Convolutional Neural Network (CNN) for image classification. shape: 150, 150, 3filters: 32kernel size: 3x3activation: ReLUpool size: 2x2units: 64kernel size: 3x3activation: ReLUpool size: 2x2units: 64activation: ReLU dropout: 0.5units: 1activation: sigmoidTrain the model for 10 epochs (Adam) withloss function binary crossentropyApply the modelon new dataoutput >= 0.5 Catoutput < 0.5 DogEvaluate the modelaccuracyResize to 150x150Path to training imagesNormalize between 0..1Read imagesRemove superfluous columnsSave modelSet up condaenvironmentEncode classeswith 0 and 1Keras Input Layer Keras Convolution2D Layer Keras Max Pooling2D Layer Keras Convolution2D Layer Keras Max Pooling2D Layer Keras Flatten Layer Keras Dense Layer Keras Dropout Layer Keras Dense Layer Keras NetworkLearner Keras NetworkExecutor Rule Engine Scorer Image Resizer Paths to images Image Calculator Image Reader(Table) Column Filter Partitioning Keras NetworkWriter Conda EnvironmentPropagation Rule Engine Visualize results The Conda Environment Propagation node ensures theexistence of a Conda environment with all packages.Another option is to setup your Python integration to usea Conda environment with all packages as describedhere: https://docs.knime.com/2021-12/deep_learning_installation_guide/index.html#dl_python_setup Read and preprocess images Train and evaluate the CNN Define CNN architecture Find the data here!https://www.kaggle.com/c/dogs-vs-cats/data Image Classification ApplicationThis workflow reads images of cats and dogs, performs some image preprocessing, and trains and evaluates a Convolutional Neural Network (CNN) for image classification. shape: 150, 150, 3filters: 32kernel size: 3x3activation: ReLUpool size: 2x2units: 64kernel size: 3x3activation: ReLUpool size: 2x2units: 64activation: ReLU dropout: 0.5units: 1activation: sigmoidTrain the model for 10 epochs (Adam) withloss function binary crossentropyApply the modelon new dataoutput >= 0.5 Catoutput < 0.5 DogEvaluate the modelaccuracyResize to 150x150Path to training imagesNormalize between 0..1Read imagesRemove superfluous columnsSave modelSet up condaenvironmentEncode classeswith 0 and 1Keras Input Layer Keras Convolution2D Layer Keras Max Pooling2D Layer Keras Convolution2D Layer Keras Max Pooling2D Layer Keras Flatten Layer Keras Dense Layer Keras Dropout Layer Keras Dense Layer Keras NetworkLearner Keras NetworkExecutor Rule Engine Scorer Image Resizer Paths to images Image Calculator Image Reader(Table) Column Filter Partitioning Keras NetworkWriter Conda EnvironmentPropagation Rule Engine Visualize results

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: jon
Created at: 2017-10-29
On NodePit since: 2022-12-16
Last update: 2024-11-21
Created with KNIME version: v4.7.0
Tags: deep learningKerasimage classificationcatdogCNN

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!