Article Introduction：We know that quantizing activations, weights and gradients into 4-bit is very valuable for speeding up neural network training. But existing 4-bit training methods require custom number formats that are not supported by contemporary hardware. In this article, Tsinghua Zhu Jun and others proposed a Transformer training method that uses the INT4 algorithm to implement all matrix multiplications. Whether the model is trained quickly or not is closely related to the requirements of activation values, weights, gradients and other factors. Neural network training requires a certain amount of calculation, and using low-precision algorithms (full quantization training or FQT training) is expected to improve computing and memory efficiency. FQT adds quantizers and dequantizers to the original full-precision computational graph and replaces expensive floating-point operations with cheap low-precision floating-point operations.

2023-07-02 comment 0  946

Article Introduction：Image Feature Extraction for SVM Training in OpenCVIn image classification tasks using Support Vector Machines (SVMs), feature extraction plays a...

2024-12-10 comment 0  449

Article Introduction：How to implement distributed algorithms and model training in PHP microservices Introduction: With the rapid development of cloud computing and big data technology, the demand for data processing and model training is increasing. Distributed algorithms and model training are key to achieving efficiency, speed, and scalability. This article will introduce how to implement distributed algorithms and model training in PHP microservices, and provide some specific code examples. 1. What is distributed algorithm and model training? Distributed algorithm and model training is a technology that uses multiple machines or server resources to perform data processing and model training simultaneously.

2023-09-25 comment 0  1436

Article Introduction：Highlights: Researchers propose a new technology called StableRep that uses images generated by artificial intelligence to train highly detailed artificial intelligence image models. StableRep is trained by using millions of labeled synthetic images, using "multiple "Positive Contrast Learning Method" to improve the learning process and apply it to the open source text-to-image model StableDiffusion-⚙️Although StableRep has achieved significant achievements in ImageNet classification, it is slow to generate images, and it is slow in both text prompts and generated images. There is a semantic mismatch between them. Webmaster’s Home (ChinaZ.com) News on November 28: Researchers from MIT and Google

2023-11-29 comment 0  969

Article Introduction：Recently, diffusion models have surpassed GAN and autoregressive models and become the mainstream choice for generative models due to their excellent performance. Text-to-image generation models based on diffusion models (such as SD, SDXL, Midjourney, and Imagen) have demonstrated the amazing ability to generate high-quality images. Typically, these models are trained at a specific resolution to ensure efficient processing and accurate model training on existing hardware. Figure 1: Comparison of using different methods to generate 2048×2048 images under SDXL1.0. [1] In these diffusion models, pattern duplication and severe artifacts often occur. For example, it is shown on the far left side of Figure 1. These problems are particularly acute beyond the training resolution.

2024-04-08 comment 0  1279

Article Introduction：Google DeepMind announced a model training method called "FunSearch" on December 15. It is said that this model can solve a series of "complex problems involving mathematics and computer science", including "upper-level problems" and "bin-packing problems". The content that needs to be rewritten is: ▲Image source Google DeepMind (the same below) FunSearch The model training method is reported to have introduced a system called "evaluator", which is used to evaluate the creative problem-solving methods output by the AI ​​model. Through repeated iterations, this method can train an AI model with stronger mathematical capabilities. GoogleDeepMind used the PaLM2 model for testing. The researchers established a dedicated code pool and used code as a model.

2023-12-15 comment 0  824

Article Introduction：Machine learning is a method of letting computers learn from data without being explicitly programmed. It uses algorithms to analyze and interpret patterns in data and then make predictions or decisions without human intervention. Understanding the concept of machine learning requires mastering basic concepts such as algorithms, training, models, and coefficients. Through machine learning, computers can learn from large amounts of data to improve their performance and accuracy. This method has been widely used in many fields, such as natural language processing, image recognition and data analysis. Mastering the knowledge of machine learning will provide us with more opportunities and challenges. Algorithm An algorithm in machine learning is a set of instructions or procedures used to solve a problem or achieve a specific task. It is a step-by-step process that helps achieve expectations

2024-01-22 comment 0  857

Article Introduction：How to implement image compression algorithm in C# Summary: Image compression is an important research direction in the field of image processing. This article will introduce the algorithm for implementing image compression in C# and give corresponding code examples. Introduction: With the widespread application of digital images, image compression has become an important part of image processing. Compression can reduce storage space and transmission bandwidth, and improve the efficiency of image processing. In the C# language, we can compress images by using various image compression algorithms. This article will introduce two common image compression algorithms:

2023-09-19 comment 0  1017

Article Introduction：How to use image processing algorithms in C++: Practical tips and code examples Introduction: Image processing is one of the important research directions in the field of computer science and engineering. It mainly involves the acquisition, processing and analysis of images. As a powerful and widely used programming language, C++ is widely used to implement image processing algorithms. This article will introduce how to use image processing algorithms in C++ and provide specific code examples to help readers better understand and apply these algorithms. 1. Image Reading and Saving Before image processing, the first step is to read

2023-09-19 comment 0  970

Article Introduction：In today's era of rapid development of computer technology, image processing technology plays an important role in various fields. In many applications of image processing, image filtering algorithms are an indispensable part. However, the speed of image filtering algorithms has been a challenge due to the dimensionality and complexity of images. This article will explore how to optimize the speed of image filtering algorithms in C++ development. First of all, for the optimization of image filtering algorithms, reasonable selection of algorithms is the first step. Common image filtering algorithms include mean filtering, median filtering, Gaussian filtering, etc. When choosing an algorithm

2023-08-22 comment 0  1071

Article Introduction：Gesture recognition is an important research area in the field of computer vision. Its purpose is to determine the meaning of gestures by parsing human hand movements in video streams or image sequences. Gesture recognition has a wide range of applications, such as gesture-controlled smart homes, virtual reality and games, security monitoring and other fields. This article will introduce the algorithms and principles used in gesture recognition models, and use Python to create a simple gesture recognition training model. Algorithms and principles used in gesture recognition models The algorithms and principles used in gesture recognition models are diverse, including models based on deep learning, traditional machine learning models, rule-based methods and traditional image processing methods. The principles and characteristics of these methods will be introduced below. 1. Model deep learning based on deep learning

2024-01-24 comment 0  1096

Article Introduction：How to optimize the effect of image processing algorithms in C++ development Summary: Image processing occupies an important position in computer science and vision technology. In C++ development, optimizing image processing algorithms can improve image processing effects and performance. This article introduces some optimization techniques, including algorithm optimization, parallelization and hardware acceleration, to help developers improve the effect of image processing algorithms. Introduction: In the development of modern science and technology, image processing plays a vital role in many fields, such as medical imaging, computer vision, artificial intelligence, etc. And C++ as a high

2023-08-22 comment 0  1247

Article Introduction：Optimizing data structures and algorithms in image processing can improve efficiency. The following optimization methods: Image sharpening: Use convolution kernels to enhance details. Image lookup: Use hash tables to quickly retrieve images. Image concurrent processing: use queues to process image tasks in parallel.

2024-05-08 comment 0  1034

Article Introduction：Golang development: Implementing efficient image processing algorithms Introduction: With the widespread application of digital images, image processing has become an important research field. For image processing algorithm requirements, an important indicator is processing speed. In this article, we will introduce how to use Golang to develop efficient image processing algorithms and provide specific code examples. 1. Advantages of Golang Golang is a programming language developed by Google and is designed to build high-performance, scalable applications. compared to other

2023-09-20 comment 0  970

Article Introduction：Golang is an efficient programming language that is widely used in network programming, distributed systems, cloud computing and other fields. In the field of graphics and image algorithms, Golang's concurrency and high performance can also exert great advantages. However, as the complexity of the algorithm increases, the caching of the algorithm becomes more and more important. This article will describe how to implement an efficient graphics and image algorithm caching mechanism in Golang. 1. The concept and principle of cache Cache (Cache) is a high-speed memory used to store calculation results. When the system needs a

2023-06-20 comment 0  1212

Article Introduction：How to Optimize Image Processing and Computer Vision Algorithms in C++ As image processing and computer vision applications become more popular, the need for efficient algorithms increases. This guide will explore effective ways to optimize image processing and computer vision algorithms in C++ and provide practical examples to demonstrate these techniques in action. Bit Operations and SIMD Bit operations and Single Instruction Multiple Data (SIMD) instructions can significantly reduce execution time. The bitset class in C++ allows fast processing of bit operations, while intrinsics and compiler optimizations enable SIMD instructions to process multiple data elements at once. Practical case: image binarization //Use bitset class for fast image binarization bit

2024-06-01 comment 0  1032

Article Introduction：How to Optimize Image Compression Algorithm Speed ​​in C++ Development Summary: Image compression is one of the widely used technologies in many computer vision and image processing applications. This article will focus on how to improve the running speed of image compression algorithms in C++ development. First, the principles of image compression and commonly used compression algorithms are introduced, and then several optimization techniques are explained in detail, such as parallel computing, vectorization, memory alignment, and algorithm optimization. Finally, the effectiveness of these optimization techniques is verified through experiments, and some practical cases and application suggestions are provided. The essential

2023-08-22 comment 0  1607

Article Introduction：Editor | Zi Luo In order to facilitate molecular property prediction, it is very important to learn effective molecular feature representation in the field of drug discovery. Recently, people have overcome the challenge of data scarcity by pre-training graph neural networks (GNN) using self-supervised learning techniques. However, there are two main problems with current methods based on self-supervised learning: the lack of clear self-supervised learning strategies and the limited capabilities of GNN. Recently, a research team from Tsinghua University, West Lake University, and Zhijiang Laboratory proposed a knowledge-guided graph. Transformer pre-training (Knowledge-guidedPre-trainingofGraphTransformer, KPGT), a self-supervised learning framework that uses significantly enhanced analysis

2023-11-23 comment 0  1271

Article Introduction：How to use Golang to train and extract features from images Introduction: In the field of computer vision, training and feature extraction from images is a very important task. By training the model, we can identify and classify images, and at the same time extract the features of the image for applications such as image retrieval and similarity calculation. Golang is an efficient and concise programming language. This article will introduce how to use Golang to train and extract features from images. Installing Necessary Libraries Before we start, we need to install some necessary libraries. First of all, Ann

2023-08-27 comment 0  1490

Article Introduction：Eigenface algorithm is a common face recognition method. This algorithm uses principal component analysis to extract the main features of faces from the training set to form feature vectors. The face image to be recognized will also be converted into a feature vector, and face recognition is performed by calculating the distance between each feature vector in the training set. The core idea of ​​this algorithm is to determine the identity of a face to be recognized by comparing its similarity with known faces. By analyzing the principal components of the training set, the algorithm can extract the vector that best represents the facial features, thereby improving the accuracy of recognition. The eigenface algorithm is simple and efficient, so the steps of the eigenface algorithm in the field of face recognition are as follows: 1. Collect a face image data set. The eigenface algorithm requires a data set containing multiple face images as a training set.

2024-01-22 comment 0  624