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preface The last article introduced how to use ncnn to deploy yolox on nano. Because the ncnn related deployment code of yolox is open source, we only need to configure the environment according to the process, modify the relevant code and compile it. The author has open source the code of four deployment methods, but there is no libtorch. Lib ...

Since many frameworks and suites have stopped supporting CUDA 10.0, CUDA 10.0 will be installed as originally 0's server is reconfigured cuda10 2 and new drive Original drive unloading 1. Stop X Server sudo service lightdm stop 2. Uninstall the previous Driver sudo /usr/bin/nvidia-uninstall After uninstallation is completed, enter N ...

Realize the step-by-step deepening from manual derivation to automatic derivation and then to the model. Implementation of two-layer neural network with numpy A fully connected ReLU neural network, a hidden layer, no bias, L2 Loss (h is hidden layer, ReLU activation function): h ...

We trained a target detection model, wanted to deploy it in the production environment, checked a lot of data, and finally chose TorchServe to deploy it. TorchServe was jointly developed by AWS and Facebook, so I didn't think much about it. There should be nothing wrong with choosing big factories. I stepped on a lot of holes in the process of ...

First clarify the meaning of several common nouns: batch, epoch and iteration Batch: usually, we divide a data set into several small sample sets, and then feed a small part to the neural network for iteration. Each small part of the sample is called a batch. Epoch: all the data in the training set are trained completely in the model (including ...

catalogue Import third party libraries parse_opt function Import third party libraries import argparse import logging import math import os import random import sys import time from copy import deepcopy from pathlib import Path import numpy as np import torch import torch.distributed as dist import torch.nn as nn import yaml from torch. ...

introduction Initially, we will initialize the parameters of the model with the init module. Now we will learn more about how to access and initialize model parameters, and how to share the same model parameters among multiple layers. We first define a multi-layer perceptron with a single hidden layer. We still use the default method to initi ...

1, TensorBoard introduction and installation   TensorBoard is a visualization tool. In the training process, we need to visualize the training process to monitor the training status of our current training. TensorBoard is a powerful visualization tool in TensorFlow, but PyTorch has supported the use of TensorBoard, supporting scalar, ...

technological process To prepare data, you need to prepare DataLoader To build the model, torch can be used to construct a deep neural network Model training Save the model, save the model and continue to use it later Evaluation of the model, use the test set to observe the quality of the model Prepare data First download the digital image d ...

Graph structure can be seen everywhere in the real world. Roads, social networks and molecular structures can be represented by graphs. Graph is one of the most important data structures we have.There are many resources today that can teach us everything we need to apply machine learning to such data.There have been many theories and materials ...