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…

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#pytorch #unet

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UNET Implementation in PyTorch | Semantic Segmentation
Dominic  Feeney

Dominic Feeney

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Semantic Segmentation with TensorFlow Keras - Analytics India Magazine

(https://analyticsindiamag.com/google-arts-culture-uses-ai-to-preserve-endangered-languages/)

Semantic Segmentation laid down the fundamental path to advanced Computer Vision tasks such as object detectionshape recognitionautonomous drivingrobotics, and virtual reality. Semantic segmentation can be defined as the process of pixel-level image classification into two or more Object classes. It differs from image classification entirely, as the latter performs image-level classification. For instance, consider an image that consists mainly of a zebra, surrounded by grass fields, a tree and a flying bird. Image classification tells us that the image belongs to the ‘zebra’ class. It can not tell where the zebra is or what its size or pose is. But, semantic segmentation of that image may tell that there is a zebra, grass field, a bird and a tree in the given image (classifies parts of an image into separate classes). And it tells us which pixels in the image belong to which class.

In this article, we discuss semantic segmentation using TensorFlow Keras. Readers are expected to have a fundamental knowledge of deep learning, image classification and transfer learning. Nevertheless, the following articles might fulfil these prerequisites with a quick and clear understanding:

  1. Getting Started With Deep Learning Using TensorFlow Keras
  2. Getting Started With Computer Vision Using TensorFlow Keras
  3. Exploring Transfer Learning Using TensorFlow Keras

Let’s dive deeper into hands-on learning.

#developers corner #densenet #image classification #keras #object detection #object segmentation #pix2pix #segmentation #semantic segmentation #tensorflow #tensorflow 2.0 #unet

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

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

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

UNET Architecture in TensorFlow 2.0 (Keras) | UNET Segmentation | Semantic Segmentation

In this video, we are going to learn about the UNET architecture from the original paper. Next, we are going to use TensorFlow 2.0 (Keras) to build the UNET architecture from scratch.

CODE: https://github.com/nikhilroxtomar/Unet-for-Person-Segmentation

U-Net: https://arxiv.org/abs/1505.04597​
ResU-Net: https://arxiv.org/pdf/1711.10684​
DoubleU-Net: https://arxiv.org/abs/2006.04868

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#tensorflow #keras #unet