Kolby  Wyman

Kolby Wyman

1596482760

Implementing UNet in Pytorch

When learning image segmentation UNet serves as one of the basic models for the segmentation. UNet is one of the most used models for image segmentation. You can see people are making a lot of changes in the Original UNet architecture like using**_ Resnet_** etc. but let’s implement the **Original UNet Architecture. **in 7 Steps

Architecture of the Unet.

The architecture of the Unet can be divided into two part**_ Left_** (Contracting path) &_ Right_ (Expansion path).

The Left part_ is just a simple convolution network. In the left part Two 3x3 Convolution layers followed by a Relu activation function are stacked together (Sequentially) and a 2x2 maxpool layer is applied after that(red arrow in image) First vertical bar in the left side in the image is not a layer but represents the input.(input image tile)_

The Right part_ is where interesting things happen. Right part also uses Two 3x3 Convolution layers stacked together (Sequentially) like left side but no Relu activation function is used and there is no maxpool layer used instead a 2x2 Transpose convolution layer is used (green arrow in image ). During the expansion path, we will take the image (copy ) from the left side and combine it with the image on the right (grey arrow in the image). Remember a sequential 3x3 convolution layers are also used in the right side so the input for that will be combination of the image from right and its previous layer (half white and blue box in the right side of the image is the combination)._

The output layer on the right side an extra convolution layer is applied (output segmentation map ).


So let’s just code the Unet architecture.

Full code :_ Github_

#unet #ai #computer-vision #pytorch #image-segmentation

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Buddha Community

Implementing UNet in Pytorch
Kolby  Wyman

Kolby Wyman

1596482760

Implementing UNet in Pytorch

When learning image segmentation UNet serves as one of the basic models for the segmentation. UNet is one of the most used models for image segmentation. You can see people are making a lot of changes in the Original UNet architecture like using**_ Resnet_** etc. but let’s implement the **Original UNet Architecture. **in 7 Steps

Architecture of the Unet.

The architecture of the Unet can be divided into two part**_ Left_** (Contracting path) &_ Right_ (Expansion path).

The Left part_ is just a simple convolution network. In the left part Two 3x3 Convolution layers followed by a Relu activation function are stacked together (Sequentially) and a 2x2 maxpool layer is applied after that(red arrow in image) First vertical bar in the left side in the image is not a layer but represents the input.(input image tile)_

The Right part_ is where interesting things happen. Right part also uses Two 3x3 Convolution layers stacked together (Sequentially) like left side but no Relu activation function is used and there is no maxpool layer used instead a 2x2 Transpose convolution layer is used (green arrow in image ). During the expansion path, we will take the image (copy ) from the left side and combine it with the image on the right (grey arrow in the image). Remember a sequential 3x3 convolution layers are also used in the right side so the input for that will be combination of the image from right and its previous layer (half white and blue box in the right side of the image is the combination)._

The output layer on the right side an extra convolution layer is applied (output segmentation map ).


So let’s just code the Unet architecture.

Full code :_ Github_

#unet #ai #computer-vision #pytorch #image-segmentation

Husam Abdullah

1621406489

UNET Implementation in PyTorch | Semantic Segmentation

In this video, we are going to implement UNET architecture in the PyTorch framework.

PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing, primarily developed by Facebook’s AI Research lab.

CODE: https://github.com/nikhilroxtomar/Sem…

Subscribe: https://www.youtube.com/c/IdiotDeveloper/featured

#pytorch #unet

Implementing Real-time Object Detection System using PyTorch and OpenCV

Hands-On Guide to implement real-time object detection system using python

The Self-Driving car might still be having difficulties understanding the difference between humans and garbage can, but that does not take anything away from the amazing progress state-of-the-art object detection models have made in the last decade.

Combine that with the image processing abilities of libraries like OpenCV, it is much easier today to build a real-time object detection system prototype in hours. In this guide, I will try to show you how to develop sub-systems that go into a simple object detection application and how to put all of that together.

Python vs C++

Reading The Video Stream

Load the Model

Scoring a Single Frame

#artificial-intelligence #python #programming #implementing real-time object detection system #implementing real-time object detection system using pytorch and opencv #pytorch

PyTorch For Deep Learning 

What is Pytorch ?

Pytorch is a Deep Learning Library Devoloped by Facebook. it can be used for various purposes such as Natural Language Processing , Computer Vision, etc

Prerequisites

Python, Numpy, Pandas and Matplotlib

Tensor Basics

What is a tensor ?

A Tensor is a n-dimensional array of elements. In pytorch, everything is a defined as a tensor.

#pytorch #pytorch-tutorial #pytorch-course #deep-learning-course #deep-learning

Facebook Gives Away This PyTorch Library For Differential Privacy

Recently, Facebook AI open-sourced a new high-speed library for training PyTorch models with differential privacy (DP) known as Opacus. The library is claimed to be more scalable than existing state-of-the-art methods.

According to the developers at the social media giant, differential privacy is a mathematically rigorous framework for quantifying the anonymisation of sensitive data. With the growing interest in the machine learning (ML) community, this framework is often used in analytics and computations.

Differential privacy constitutes a strong standard for privacy guarantees for algorithms on aggregate databases. It is usually defined in terms of the application-specific concept of adjacent databases. The framework has several properties that make it particularly useful in applications, such as group privacy, robustness to auxiliary information, among others.

#developers corner #differential privacy #facebook ai research #facebook differential privacy #opacus #pytorch #pytorch library #pytorch library opacus