Detect Eye Disease With Pytorch

Deep learning is part of a broader family of machine learning methods based on Artificial Neural Networks (ANN). Deep learning today is ubiquitous, it is used in different application, from image classification to speech recognition. In this blog post I’m going to show you how to build a simple neural network to detect different eye diseases from Retinal optical coherence tomography (OCT) images using pytorch.

This is the 5 assignment for the course Zero to GANs on freeCodeCam.com

The dataset

OCT is an imaging technique used to capture high-resolution cross sections of the retinas of living patients. Approximately 30 million OCT scans are performed each year, and the analysis and interpretation of these images takes up a significant amount of time.

The dataset is taken from kaggle, it’s organized into 3 folders (train, test, val) and contains subfolders for each image category: choroidal neovascularization (CNV), diabetic macular edema (DME), multiple drusen present in early AMD (DRUSEN), and normal retina with preserved foveal contour and absence of any retinal fluid/edema (NORMAL).

Load and preprocess the images

First we’re going to load all the libraries and specify the function that we will use to load our data and our model on the GPU.

Then we’re going to parse all the image in the train folder in order to create two vectors containing the mean and the standard deviation of each channel of the training images. We’re gonna use those stats to normalize the images.

Now we load the data using pytorch. Each image is center-croppped to a size of 490x490 pixels (in order to have uniform size between each image), is converted to a tensor and then normalized.

#zero-to-gan #image-classification #deep-learning #pytorch #deep learning

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Detect Eye Disease With Pytorch

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

Detect Eye Disease With Pytorch

Deep learning is part of a broader family of machine learning methods based on Artificial Neural Networks (ANN). Deep learning today is ubiquitous, it is used in different application, from image classification to speech recognition. In this blog post I’m going to show you how to build a simple neural network to detect different eye diseases from Retinal optical coherence tomography (OCT) images using pytorch.

This is the 5 assignment for the course Zero to GANs on freeCodeCam.com

The dataset

OCT is an imaging technique used to capture high-resolution cross sections of the retinas of living patients. Approximately 30 million OCT scans are performed each year, and the analysis and interpretation of these images takes up a significant amount of time.

The dataset is taken from kaggle, it’s organized into 3 folders (train, test, val) and contains subfolders for each image category: choroidal neovascularization (CNV), diabetic macular edema (DME), multiple drusen present in early AMD (DRUSEN), and normal retina with preserved foveal contour and absence of any retinal fluid/edema (NORMAL).

Load and preprocess the images

First we’re going to load all the libraries and specify the function that we will use to load our data and our model on the GPU.

Then we’re going to parse all the image in the train folder in order to create two vectors containing the mean and the standard deviation of each channel of the training images. We’re gonna use those stats to normalize the images.

Now we load the data using pytorch. Each image is center-croppped to a size of 490x490 pixels (in order to have uniform size between each image), is converted to a tensor and then normalized.

#zero-to-gan #image-classification #deep-learning #pytorch #deep learning

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

Chando Dhar

Chando Dhar

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Deep Learning Project : Real Time Object Detection in Python & Opencv

Real Time Object Detection in Python And OpenCV

Github Link: https://github.com/Chando0185/Object_Detection

Blog Link: https://knowledgedoctor37.blogspot.com/#

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#python project #object detection #python opencv #opencv object detection #object detection in python #python opencv for object detection

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