Programming VIP

PointNet + + needs to restore the down sampled points to the same number of points as the input when processing the segmentation task, so as to facilitate the prediction of each point. But in the paper, I only give a simple description and formula, which is not very easy to understand, so I record my understanding process here. 1. Purpose of F ...

About parameter acquisition: it has been proposed in the previous blog, please refer to For relevant links, please click 1, Analysis input and output 1. The handwriting input is a 28x28 black-and-white picture, so the input is 784 X 2. The output is the probability of identifying numbers 0-9, so there are 10 outputs 3. The input can only ...

The previous article explained the logistic regression model of deep learning. This article will next talk about the vectorization of logistic regression and the basic code required for compilation.   1.sigmoid function:                                                       The sigmoid function can be compiled using python's math libraryH ...

Official explanation: Dataloader combines dataset & sampler to provide iterable data Main parameters: 1. Dataset: this dataset must be torch.utils.data.Dataset itself or a class inherited from it The main method is  __ getitem__(self, index) used to retrieve data according to the index index 2,batch_size: how many pieces of data sh ...

preface Using Baidu's open source paddleX tool, we can easily and quickly train a deep network model of target detection, image classification, instance segmentation and semantic segmentation using our own labeled data. This paper mainly records how to use pddleX to train a simple ppyolo for detecting cats and dogs in the whole process_ Tiny m ...

1. Gradually construct convolution network The basic network architecture built this time: Note: for each forward propagation operation, there will be corresponding backward propagation. The parameters of forward propagation will be stored, and these parameters will be used to calculate the gradient in the backward propagation process. 2. Conv ...

  Author home page( Silicon based workshop of slow fire rock sugar): Slow fire rock sugar (Wang Wenbing) blog silicon based workshop of slow fire rock sugar _csdnblog Website of this article: https://blog.csdn.net/HiWangWenBing/article/details/120600611 catalogue Introduction deep learning model framework Chapter 1 business area analy ...

The summary is taken from Mr. Gao GitHub https://github.com/OUCTheoryGroup/colab_demo/blob/master/202003_models/MobileNetV1_CIFAR10.ipynb https://github.com/OUCTheoryGroup/colab_demo/blob/master/202003_models/MobileNetV2_CIFAR10.ipynb MobileNet v1 The core of Mobilenet v1 is to split the convolution into two parts: epthwise+Pointwise. Dept ...

        The traditional statistical language model is a nonparametric model, that is, the conditional probability is estimated directly by counting, but the main disadvantage of this nonparametric model is poor generalization and can not make full use of similar context          The ...

1.4. Training As long as layers are supported and parameter constraints are met, existing cafe and TF slim models can be imported. However, these models usually include dense weight matrices. In order to take advantage of some advantages of tidl lib and obtain 3x-4x performance improvement (for convolution layer), it is necessary to use caffe J ...