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As data scientists or machine learning practitioners, integrating interpretability into machine learning models can help decision makers and other stakeholders have more visibility and understand the interpretation of model output decisions.In this article, I will introduce two models, life and shake, which can help understand the decision-maki ...

preface The pioneering work of the VIT model is to use a pure transformer structure, as shown in the title of the paper: AN IMAGE IS WORTH 16X16 WORDS, which embeds the pictures into a series of sequence s, and realizes the effect comparable to the SOTA model in CNN through multiple encoder structures and head s. Image classification t ...

1, Image augmentation Definition & Interpretation: By making a series of random changes to the training image, similar but different training samples are generated, so as to expand the scale of the training data set.Randomly changing the training samples can reduce the dependence of the model on some attributes, so as to improve the g ...

1 environmental installation To learn Python development, you must first learn to install the python environment. I generally use Anaconda + pychar as the development environment. Anaconda is an open source Python Release that contains conda More than 180 science packages and their dependencies, such as, Python, etc. Because it contains a ...

Introduction to docker image docker images are layered, with underlying online overlay downloads. dockerfille generates images in the form of text. Its advantage is that you can view the generation process after generating images. Compared with docker commit, it is more secure and reliable. When using dockerfile, you must write a file with t ...

1, Training environment darknet compiled under Windows 10 Compilation process: https://blog.csdn.net/weixin_54603153/article/details/119980266?spm=1001.2014.3001.5501) Source address: https://github.com/AlexeyAB/darknet 2, Make your own dataset 1. First, create a folder for the dataset under the Darknet master folder Annotations tag x ...

02 introduction to deep learning What is the field of perception: the stage at which people can quickly perceive and process (English to Chinese). Deep learning, computer vision and natural language processing are the three largest contents in the field of AI. Application of deep learning Image classification (IMAGENET), at present, the erro ...

Original code from t ensorflow Tensorflow learning notes (load pictures with tf.data) This tutorial provides an example of how to use TF Data loading a simple example of an image. Importing modules, configuring import tensorflow as tf tf.data is used for data set construction and preprocessing AUTOTUNE = tf.data.experimental.AUTOTUNE ...

I. Introduction 1 Overview BP (Back Propagation) neural network was proposed by the scientific research team headed by Rumelhart and McCelland in 1986. See their paper learning representations by Back Propagation errors published in Nature. BP neural network is a multilayer feedforward network trained by error back propagation algorithm. It i ...

1, SIFT introduction SIFT, scale invariant feature transformation, is a description used in the field of image processing. This description has scale invariance and can detect key points in the image. It is a local feature descriptor. 1. Features of SIFT algorithm (1) It has good stability and invariance, can adapt to the changes of rotation, ...