Examples of Models Trained with Pytorch

Not long after the Pytorch release, it became clear that there’s another strong player in the segment of deep learning frameworks that can make a good competition to the established leader.

5 min read, Aug 13

PyTorch is an open-source deep learning platform binding together Python interface and C++ backend to ensure the required scalability, flexibility, and speed for the modeling, research, and experiments. From version 1.0 it offers execution based on dynamic computational graphs raising the level of the development agility.

The framework frontend is built to simplify and optimize distributed training, mode switching and mobile deployment. There are separate backends to ensure appropriate code running on CPU and GPU making it a convenient choice for constrained systems.

Additional improvements to Pytorch model building that are present include tape-based autograd, tensor computing accelerated by GPU and easy debugging using standard tools. Such an approach makes it more suitable for prototyping and small projects compared to other available frameworks for artificial neural network development.

They also cover an easy start: a selection of installation options and a gift-box toolkit full of datasets, libraries, an ML compiler and even pre-trained models the growing platform community is contributing to.

Why use PyTorch in 2019

The PyTorch finds the widest application in deep learning which algorithms have proven its efficiency for data science, computer vision, natural language, and image processing needs in various industries and overall AI development.

Considering an ongoing trend of digital transformation in the fields like logistics, retail and other machine learning and in particular, DL methods more and more often applied to business process automation.

If mentioned-above features aren’t enough for a Pytorch model apply-or-not choice, explore the following advantages in comparison of Tensorflow. Perhaps they will be more convincing to bring you closer to a destination - to hire a deep learning engineer majoring in Pytorch.

Graphs defining

Tensorflow. Based on tensors it considers any developing model as a directed acyclic graph (DAG) that should be defined statically before running (except from TensorFlow Fold). External data can be integrated only during runtime by substituting two specific tensors.

Pytorch. Its dynamic approach to the task gives more freedom in modeling: no moment limits or specialized elements required for definition, modification, and execution. It makes the framework a more convenient choice for recursive and tree recurrent neural networks (RNN).

Modular networks

Tensorflow. Building a complicated model can be challenging due to the platform possibilities only for monolithic development. But in combination with a static approach, it ensures its easier understanding disregarding its size.

Pytorch. With this framework, modeling becomes a constructor-like activity. A network can be developed step by step. It simplifies the development and making training shorter without noticeable performance losses.

Model deployment

Tensorflow. Before the latest Pytorch edition the library had another valuable point in the competition. In addition to parallel and distributed training, it comes with mobile platform support making it completely production-ready.

Pytorch. The version 1.1 filled the mentioned gaps including mobile deployment heating up the competition. It also introduced integration with Tensorboard - appreciated Tensorflow visualization tool optimizing workflow environment for Python development fandom.

The latest version of the Facebook toolkit was a big step forward to taking over its Google ancestor, But looks like the competition of PyTorch Tensorflow 2019 won’t change the power balance. But by opting for contributing to the list of Pytorch use cases you sign up for future benefits.

Pretrained models in PyTorch: what they can do

To avoid misunderstanding a pre-trained status has a model that was trained on a large benchmark dataset for solving a particular issue and exist in the initial framework ecosystem.

Their quality allows substitution the reinventing the bicycle part in the project development with transfer learning enabling implementation of the simple processes using required neural networks. Thanks to them can be performed:

Image classification

For as common task the hired computer vision engineer or image processing specialist won’t require high qualification. He or she can just use one of Pytorch example models pretrained in ImageNet dataset like ResNet34.

The perfect example of a PyTorch implementation of detection is Springs project - an insect classifier that was turned into a web app using Django. By comparing an input photo to the existing database of Google images it can help determine the creature kind and provide its application to a user.

ResNet 34

Object detection and tracking

This task of CV or DIP is commonly used for development security and in particular anti-theft solutions. Some of the available Pytorch model examples can become a basis of a custom deep learning model trained for detection and monitoring of certain items, for instance, in warehousing facilities.

Springs team ensured the usability of ResNet34 for these purposes as well by implementing a box counter solution for a chicken delivery hub in Ukraine. It allowed minimizing the number of thefts during weighing and loading products to trucks.

The available dataset also enables simple enough chatbot development as well as the implementation of other NLP projects. Mentioned projects as a PyTorch pretrained models example of application prove the affordability of creation of such solutions even by the SME sector since modeling of a neural network from scratch is quite time-consuming, complicated and ergo pricey.

With Pytorch build model samples became much easier. But it’s not the case where simplicity is achieved by lowering the quality or performance level. The latest update allowed the framework almost to catch up with a proven leader Tensorflow. It shows its potential based on already established flexibility to be the most used deep learning framework in the nearest future.

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