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Using the resnet-50 model on the caltech101 image dataset to classify images and detect objects semantically.
Using the resnet-50 model on the caltech101 image dataset to classify images and detect objects semantically. ...learn more
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This example shows how to use transfer learning to retrain ResNet-50, a pretrained convolutional neural network, to classify a new set of images.In this case the network is used to identify objects from the Caltech-101 dataset. Transfer learning is commonly used in deep learning applications. One can take a pretrained network and use it as a starting point to learn a new task. Fine-tuning a network with transfer learning is usually much faster and easier than training a network with randomly initialized weights from scratch.
Object Detection has been achieved by implementing the following steps in MATLAB
https://github.com/ritwikraha/Object-Detection-using-Transfer-Learning
Using the resnet-50 model on the caltech101 image dataset to classify images and detect objects semantically.