This workflow performs classification on some sample images using the ResNet50 deep learning network architecture, trained on ImageNet, via Keras (TensorFlow). In 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) You also need a local Python installation that includes Keras. Please refer to https://www.knime.com/deeplearning#keras for installation recommendations and further information. Acknowledgements: The enclosed network was originally released by He et al.  (https://github.com/KaimingHe/deep-residual-networks) under the MIT license (https://github.com/KaimingHe/deep-residual-networks/blob/master/LICENSE). It was created using keras.applications.resnet50.ResNet50 and its weights were fetched from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5 . The enclosed pictures were modified from Caltech 101 dataset (http://www.vision.caltech.edu/Image_Datasets/Caltech101/Caltech101.html) .  Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. Deep Residual Learning for Image Recognition. arXiv:1512.03385, 2015.  Chollet, Francois and others. Keras. https://github.com/fchollet/keras. 2015.  L. Fei-Fei, R. Fergus and P. Perona. Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. IEEE. CVPR 2004, Workshop on Generative-Model Based Vision. 2004
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