Husam Abdullah

1601364324

Multiclass Segmentation using Unet in TensorFlow (Keras)| Semantic Segmentation

In this video, we are working on the multiclass segmentation using Unet architecture. For this task, we are going to use the Oxford IIIT Pet dataset.

What is semantic segmentation?

The goal of semantic image segmentation is to label each pixel of an image with a corresponding class. It is also called Dense prediction.

What is U-Net?

U-Net is a fully convolutional neural network that was developed by Olaf Ronneberger. It was especially developed for the purpose of biomedical image segmentation.

Sorce Code : https://github.com/nikhilroxtomar/Multiclass-Segmentation-in-Unet

Subscribe : https://www.youtube.com/channel/UClkqp31PHke-f8b8mjiiY-Q

#tensorflow #keras #unet

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Multiclass Segmentation using Unet in TensorFlow (Keras)| Semantic Segmentation
Dominic  Feeney

Dominic Feeney

1621242214

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

Husam Abdullah

1601364324

Multiclass Segmentation using Unet in TensorFlow (Keras)| Semantic Segmentation

In this video, we are working on the multiclass segmentation using Unet architecture. For this task, we are going to use the Oxford IIIT Pet dataset.

What is semantic segmentation?

The goal of semantic image segmentation is to label each pixel of an image with a corresponding class. It is also called Dense prediction.

What is U-Net?

U-Net is a fully convolutional neural network that was developed by Olaf Ronneberger. It was especially developed for the purpose of biomedical image segmentation.

Sorce Code : https://github.com/nikhilroxtomar/Multiclass-Segmentation-in-Unet

Subscribe : https://www.youtube.com/channel/UClkqp31PHke-f8b8mjiiY-Q

#tensorflow #keras #unet

Husam Abdullah

1612105820

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

Subscribe: https://www.youtube.com/channel/UClkqp31PHke-f8b8mjiiY-Q

#tensorflow #keras #unet

Husam Abdullah

1610518600

UNet for Person Segmentation || UNet Segmentation using TensorFlow Keras || Deep Learning

In this video, we are going to use the famous UNet architecture for segmenting person from an image. For the person segmentation, we are going to use the person segmentation dataset.

The U-Net is built for Biomedical Image Segmentation. It is the base model for any segmentation task. It follows an encoder-decoder approach. It used skip connection to get the local information during downsampling path and use it during the upsampling path.

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

Subscribe: https://www.youtube.com/channel/UClkqp31PHke-f8b8mjiiY-Q

#unet #tensorflow #keras #python #deep-learning

Husam Abdullah

1620095942

Retina Blood Vessel Segmentation using UNET in TensorFlow 2.0 (Keras) | Image Segmentation

In this video, we are going to use the DRIVE (Digital Retinal Images for Vessel Extraction) dataset for Retina Vessel Segmentation. This complete program is built in TensorFlow 2.0 framework using Keras API.

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

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

#tensorflow #keras #unet