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

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

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

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

Marget D

Marget D

1618317562

Top Deep Learning Development Services | Hire Deep Learning Developer

View more: https://www.inexture.com/services/deep-learning-development/

We at Inexture, strategically work on every project we are associated with. We propose a robust set of AI, ML, and DL consulting services. Our virtuoso team of data scientists and developers meticulously work on every project and add a personalized touch to it. Because we keep our clientele aware of everything being done associated with their project so there’s a sense of transparency being maintained. Leverage our services for your next AI project for end-to-end optimum services.

#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services

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

Video Segmentation using UNET in TensorFlow 2.0 (Keras) | UNET Segmentation | Deep Learning

In this video, we are going to use the UNET model that is trained on the Person Segmentation dataset for video segmentation.

In this tutorial, we are going to extract each frame from a video, process them and give them to the UNET. The UNET gives us the mask, using those mask, we extract the segmented part. Later, we join all the segmented part frame to form a video.

The UNET 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

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

#tensorflow #keras #deep-learning

Mikel  Okuneva

Mikel Okuneva

1603735200

Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020

The Deep Learning DevCon 2020, DLDC 2020, has exciting talks and sessions around the latest developments in the field of deep learning, that will not only be interesting for professionals of this field but also for the enthusiasts who are willing to make a career in the field of deep learning. The two-day conference scheduled for 29th and 30th October will host paper presentations, tech talks, workshops that will uncover some interesting developments as well as the latest research and advancement of this area. Further to this, with deep learning gaining massive traction, this conference will highlight some fascinating use cases across the world.

Here are ten interesting talks and sessions of DLDC 2020 that one should definitely attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time


Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

**About: **Adversarial Robustness in Deep Learning is a session presented by Dipanjan Sarkar, a Data Science Lead at Applied Materials, as well as a Google Developer Expert in Machine Learning. In this session, he will focus on the adversarial robustness in the field of deep learning, where he talks about its importance, different types of adversarial attacks, and will showcase some ways to train the neural networks with adversarial realisation. Considering abstract deep learning has brought us tremendous achievements in the fields of computer vision and natural language processing, this talk will be really interesting for people working in this area. With this session, the attendees will have a comprehensive understanding of adversarial perturbations in the field of deep learning and ways to deal with them with common recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

**About: **Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in technology degree in Mathematical Modeling and Simulation and has the interest to research in the field of artificial intelligence, learning-based systems, machine learning, etc. In this paper presentation, he will talk about the common problem of class imbalance in medical diagnosis and anomaly detection, and how the problem can be solved with a deep learning framework. The talk focuses on the paper, where he has proposed a synergistic over-sampling method generating informative synthetic minority class data by filtering the noise from the over-sampled examples. Further, he will also showcase the experimental results on several real-life imbalanced datasets to prove the effectiveness of the proposed method for binary classification problems.

Default Rate Prediction Models for Self-Employment in Korea using Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who is a PhD in Computer Science, and works in the big data centre of Korea Credit Information Services. This talk will introduce the attendees with machine learning and deep learning models for predicting self-employment default rates using credit information. He will talk about the study, where the DNN model is implemented for two purposes — a sub-model for the selection of credit information variables; and works for cascading to the final model that predicts default rates. Hong’s main research area is data analysis of credit information, where she is particularly interested in evaluating the performance of prediction models based on machine learning and deep learning. This talk will be interesting for the deep learning practitioners who are willing to make a career in this field.


#opinions #attend dldc 2020 #deep learning #deep learning sessions #deep learning talks #dldc 2020 #top deep learning sessions at dldc 2020 #top deep learning talks at dldc 2020