Deep Neural Network (DNN) is an artificial neural network, which is characterized by having multiple layers of neurons, which can better handle complex nonlinear problems. In Java, we can use open source libraries such as Deeplearning4j (DL4J) to implement deep neural networks. In DL4J, a basic deep neural network consists of multiple layers, each containing […]
Tag: deep-neural-network
No framework, just use numpy to write a wonderful chapter of DNN (Deep Neural Networks)
1. Overview A Logistics Regression can be thought of as a simple neural network consisting of only one hidden layer (Hidden Layer) (as shown in the figure below). However, Logistic Regression is often very sensitive to outliers, and it is difficult to deal with polyphenols. Therefore, people were inspired by human brain nerves and established […]
Deep Neural Networks – Convert a TensorFlow piecewise model and start using OpenCV v4.8.0
Goals In this tutorial you will learn how to Convert a TensorFlow (TF) segmentation model Run the converted TensorFlow model using OpenCV Evaluate TensorFlow and OpenCV DNN models We will discuss the above points using the DeepLab architecture as an example. Introduction Apart from the graph optimization stage, the key concepts involved in TensorFlow classification […]
Deep Neural Networks – Convert PyTorch Classification Model and Launch OpenCV v4.8.0 with OpenCV C++
Previous tutorial: Convert a PyTorch classification model and publish with OpenCV Python Original author Anastasia Murzova Compatibility OpenCV >= 4.5 Goals In this tutorial you will learn how to Convert PyTorch classification model to ONNX format Run the converted PyTorch model using the OpenCV C/C++ API Provide model inference We will discuss the above points […]
Deep Neural Networks – Convert PyTorch Classification Model and Launch OpenCV v4.8.0 with OpenCV Python
Previous tutorial: How to run a custom OCR model Next tutorial: Convert PyTorch classification model and launch with OpenCV C++ Original author Anastasia Murzova Compatibility OpenCV >= 4.5 Goals In this tutorial you will learn how to Convert PyTorch classification model to ONNX format Run the converted PyTorch model using the OpenCV Python API Evaluate […]
Deep Neural Networks – Convert PyTorch Segmentation Model and Start with OpenCV v4.8.0
Goals In this tutorial you will learn how to Convert PyTorch segmentation model Run the converted PyTorch model using OpenCV Evaluate PyTorch and OpenCV DNN models We will discuss the above points using the FCN ResNet-50 architecture as an example. Introduction The key points involved in PyTorch classification and segmentation models and the OpenCV API’s […]
Deep Neural Networks – Convert TensorFlow Classification Model and Release with OpenCV Python OpenCV v4.8.0
Original author Anastasia Murzova Compatibility OpenCV >= 4.5 Goals In this tutorial you will learn how to Get a frozen graph of a TensorFlow (TF) classification model Run the converted TensorFlow model using the OpenCV Python API Get evaluation results for TensorFlow and OpenCV DNN models We will discuss the above points using the MobileNet […]
Deep Neural Networks – How to run deep networks on Android devices OpenCV v4.8.0
Previous tutorial: Using OpenCV with OpenVINO Next tutorial: YOLO DNNs Original author Dmitry Kurtaev Compatibility OpenCV >= 3.3 Introduction In this tutorial, you will learn how to use the OpenCV deep learning module to run a deep learning network on an Android device. This tutorial has been written for the following versions of the corresponding […]
Graduation project – Research on garbage identification and classification algorithm based on Pytorch deep neural network
In modern social life and production activities, it is inevitable to generate huge amounts of various wastes. Our country’s population and economic aggregate rank among the top in the world. Therefore, we are bound to face the problem of huge amounts of garbage disposal. How to effectively classify garbage through artificial intelligence has become a […]
Deep Neural Network-DNN
Earlier we introduced neural networks, including neuron functions, activation functions, forward algorithms, backpropagation algorithms, gradient descent, etc. These contents are basically the scope of traditional neural networks. Geoffrey Hinton proposed a method called “deep belief” Network” neural network can use the “greedy layer-by-layer pre-training” strategy to effectively train the neural network. Subsequently, this method also […]