Top 10 Pretrained Models to get you Started with Deep Learning (Part 1 - Computer Vision)

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Pretrained models are a wonderful source of help for people looking to learn an algorithm or try out an existing framework. Due to time restrictions or computational restraints, it's not always possible to build a model from scratch which is why pretrained models exist! You can use a pretrained model as a benchmark to either improve the existing model, or test your own model against it. The potential and possibilities are vast. In this article, we will look at various pretrained models in Keras that have applications in computer vision.


ImageNet: VGGNet, ResNet, Inception, and Xception with Keras - PyImageSearch

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A few months ago I wrote a tutorial on how to classify images using Convolutional Neural Networks (specifically, VGG16) pre-trained on the ImageNet dataset with Python and the Keras deep learning library. The pre-trained networks inside of Keras are capable of recognizing 1,000 different object categories, similar to objects we encounter in our day-to-day lives with high accuracy. Back then, the pre-trained ImageNet models were separate from the core Keras library, requiring us to clone a free-standing GitHub repo and then manually copy the code into our projects. This solution worked well enough; however, since my original blog post was published, the pre-trained networks (VGG16, VGG19, ResNet50, Inception V3, and Xception) have been fully integrated into the Keras core (no need to clone down a separate repo anymore) -- these implementations can be found inside the applications sub-module. Because of this, I've decided to create a new, updated tutorial that demonstrates how to utilize these state-of-the-art networks in your own classification projects.


Image Classification using Pre-trained Models in PyTorch

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This post is part of the series in which we are going to cover the following topics. In the previous blog we discussed about PyTorch, it's strengths and why should you learn it. We also had a brief look at Tensors – the core data structure in PyTorch. In this blog, we will jump into some hands-on examples of using pre-trained networks present in TorchVision module for Image Classification. Torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.


Build Your First Deep Learning Classifier using TensorFlow: Dog Breed Example

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In this article, I will present several techniques for you to make your first steps towards developing an algorithm that could be used for a classic image classification problem: detecting dog breed from an image. By the end of this article, we'll have developed code that will accept any user-supplied image as input and return an estimate of the dog's breed. Also, if a human is detected, the algorithm will provide an estimate of the dog breed that is most resembling. This project was completed as part of Udacity's Machine Learning Nanodegree (GitHub repo). Convolutional neural networks (also refered to as CNN or ConvNet) are a class of deep neural networks that have seen widespread adoption in a number of computer vision and visual imagery applications.


Build Your First Deep Learning Classifier using TensorFlow: Dog Breed Example

#artificialintelligence

In this article, I will present several techniques for you to make your first steps towards developing an algorithm that could be used for a classic image classification problem: detecting dog breed from an image. By the end of this article, we'll have developed code that will accept any user-supplied image as input and return an estimate of the dog's breed. Also, if a human is detected, the algorithm will provide an estimate of the dog breed that is most resembling. This project was completed as part of Udacity's Machine Learning Nanodegree (GitHub repo). Convolutional neural networks (also refered to as CNN or ConvNet) are a class of deep neural networks that have seen widespread adoption in a number of computer vision and visual imagery applications.