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Another problem, related to deep networks is that even if you get the data, it takes a large amount of time to train the network hundreds of hours. Thus, it takes a lot of time, money and effort to train a deep network successfully. Fortunately, we can leverage the models already trained on very large amounts of data for difficult tasks with thousands of classes. The models have been trained on millions of images and for hundreds of hours on powerful GPUs.

Most often we use these models as a starting point for our training process, instead of training our own model from scratch. Apart from the ILSVRC winners, many research groups also share their models which they have trained for similar tasks, e.

Keras comes bundled with many models. A trained model has two parts — Model Architecture and Model Weights. The weights are large files and thus they are not bundled with Keras. However, the weights file is automatically downloaded one-time if you specify that you want to load the weights trained on ImageNet data. It has the following models as of Keras version 2. In the above code, we first import the python module containing the respective models. Then we load the model architecture and the imagenet weights for the networks.

Keras also provides an image module which provides functions to import images and perform some basic pre-processing required before feeding it to the network for prediction. We will use the keras functions for loading and pre-processing the image. Specificallly, we perform the following steps on an input image:. Once we have the image in the right format, we can feed it to the network and get the predictions.

The image we got in the previous step should be normalized by subtracting the mean of the ImageNet data. This is because the network was trained on the images after this pre-processing. We follow the following steps to get the classification results. We will see how to train a classifier using these same models with our own data to recognize any other set of objects which are not present in the ILSVRC dataset.

This would be the topic of our next two posts. Stay tuned! I am using vgg16 and resnet50 pretrained models on validation dataset of ImageNet. Can you help me why there is such large difference? Following is the code I used. Because i am using the same image for other models too. Is it possible to load more than one image? How can I preform multi-image analysis? Your email address will not be published. Skip to primary navigation Skip to main content Skip to primary sidebar Skip to footer.

Learn More. Download Code To easily follow along this tutorial, please download code by clicking on the button below. Download Code. PIL image size , numpy array size , , 3 image batch size 1, , , 3. Alternately, sign up to receive a free Computer Vision Resource Guide. Download Example Code. Comments Thanks for this article. Hi Vikas, Thank you for providing Great article.

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