Tensorflow: How to Retrain an Image Classifier for New Categories. Use of a GPU requires the Parallel Computing Toolbox. Using a CUDA-capable NVIDIA GPU is highly recommended for running this example.
"Decaf: A deep convolutional activation feature for generic visual recognition." arXiv preprint arXiv:1310.1531 (2013). Note: This example requires Deep Learning Toolbox, Statistics and Machine Learning Toolbox, and Deep Learning Toolbox Model for ResNet-50 Network. "Very deep convolutional networks for large-scale image recognition." arXiv preprint arXiv:1409.1556 (2014). "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems.
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. "Imagenet: A large-scale hierarchical image database." Computer Vision and Pattern Recognition, 2009. Matlab Tutorial Matlab is one of the best tools for designing machine learning algorithms and many of the class assignments and class projects will be. The category classifier will be trained on images from a Flowers Dataset. You will learn to use deep learning techniques in MATLAB for image recognition. For information about the supported compute capabilities, see GPU Computing Requirements (Parallel Computing Toolbox). Deep Learning Onramp This free, two-hour deep learning tutorial provides an interactive introduction to practical deep learning methods. Use of a GPU requires the Parallel Computing Toolbox™. Using a CUDA-capable NVIDIA™ GPU is highly recommended for running this example. Note: This example requires Deep Learning Toolbox™, Statistics and Machine Learning Toolbox™, and Deep Learning Toolbox™ Model for ResNet-50 Network. The difference here is that instead of using image features such as HOG or SURF, features are extracted using a CNN. For example, the Image Category Classification Using Bag of Features example uses SURF features within a bag of features framework to train a multiclass SVM. This approach to image category classification follows the standard practice of training an off-the-shelf classifier using features extracted from images. In this example, images from a Flowers Dataset are classified into categories using a multiclass linear SVM trained with CNN features extracted from the images. An easy way to leverage the power of CNNs, without investing time and effort into training, is to use a pretrained CNN as a feature extractor. These feature representations often outperform hand-crafted features such as HOG, LBP, or SURF. From these large collections, CNNs can learn rich feature representations for a wide range of images. CNNs are trained using large collections of diverse images. A Convolutional Neural Network (CNN) is a powerful machine learning technique from the field of deep learning.
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