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01_​Preprocess_​image_​data

Preprocess image data
KNIME Deep Learning - Classify Cats and Dogs In this series of workflows we want to demonstrate how to solve an image classification problem using KNIME Deep Learning - Keras Integration.We want to train a model to distinguish cats and dogs on images. Please note: The workflow series is heavily inspired by the great blog-post of François Chollet (see https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html.)1. Workflow 01 Preprocess image data: In this workflow we pre-process and augment the image data, which we will use throughout the followingexample workflows. You can download the data from https://www.kaggle.com/c/dogs-vs-cats/data (file train.zip) and unzip it to a desired location. 2. Workflow 02 Train simple CNN3. Workflow 03 Fine-tune ResNet50In order to run the example, please make sure you have the following KNIME extensions installed:- KNIME Deep Learning - Keras Integration (Labs)- KNIME Image Processing (Community Contributions Trusted)- KNIME Image Processing - Deep Learning Extension (Community Contributions Trusted)- KNIME Image Processing - Python Extension (Community Contributions Trusted)- KNIME Streaming Execution (Labs)You also need a local Python installation that includes Keras. Please refer to https://www.knime.com/deeplearning#keras for installationrecommendations and further information. Partition the datainto training andtest set.Read, normalize and resizethe input images.Select folder ofunzipped train.zipFirst 12500 rowsare cats, rest dogs.We only work with 4000 randomly selected images.You can adjust the numberas desired.Write training data to into data folder.Sort bypath name.Write test data to into data folder.Partitioning Load and preprocessimages (Local Files) List Files Rule Engine Row Sampling Table Writer Sorter Augmentation Table Writer KNIME Deep Learning - Classify Cats and Dogs In this series of workflows we want to demonstrate how to solve an image classification problem using KNIME Deep Learning - Keras Integration.We want to train a model to distinguish cats and dogs on images. Please note: The workflow series is heavily inspired by the great blog-post of François Chollet (see https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html.)1. Workflow 01 Preprocess image data: In this workflow we pre-process and augment the image data, which we will use throughout the followingexample workflows. You can download the data from https://www.kaggle.com/c/dogs-vs-cats/data (file train.zip) and unzip it to a desired location. 2. Workflow 02 Train simple CNN3. Workflow 03 Fine-tune ResNet50In order to run the example, please make sure you have the following KNIME extensions installed:- KNIME Deep Learning - Keras Integration (Labs)- KNIME Image Processing (Community Contributions Trusted)- KNIME Image Processing - Deep Learning Extension (Community Contributions Trusted)- KNIME Image Processing - Python Extension (Community Contributions Trusted)- KNIME Streaming Execution (Labs)You also need a local Python installation that includes Keras. Please refer to https://www.knime.com/deeplearning#keras for installationrecommendations and further information. Partition the datainto training andtest set.Read, normalize and resizethe input images.Select folder ofunzipped train.zipFirst 12500 rowsare cats, rest dogs.We only work with 4000 randomly selected images.You can adjust the numberas desired.Write training data to into data folder.Sort bypath name.Write test data to into data folder.Partitioning Load and preprocessimages (Local Files) List Files Rule Engine Row Sampling Table Writer Sorter Augmentation Table Writer

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Created by: jon
Created at: 2017-10-29
On NodePit since: 2020-05-14
Last update: 2024-04-18
Created with KNIME version: v4.1.2
Tags: deep learningKerasimage classificationcatdog

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