Table of Contents Solve ModuleNotFoundError: No module named ‘keras_resnet’ Problem Description solution Method 1: Install the keras_resnet module Method 2: Check the module name Method 3: Check the path and environment configuration Summarize Solving ModuleNotFoundError: No module named ‘keras_resnet’ When using Python for deep learning development, various module import errors are often encountered. One of […]
Tag: resnet
Solve from nets import inception_resnet_v2 ModuleNotFoundError: No module named nets
Table of Contents introduction: Problem background: Solution: Method 1: Check if the module is installed: Method 2: Use pip to install the module: Method 3: Check the Python environment and path: Method 4: Check for name conflicts and import statements: Method 5: Use a virtual environment: in conclusion: Introduction: When developing in Python, we sometimes […]
Deep learning convolutional neural image classification to achieve bird recognition including training code and bird data set (supports repVGG, googlenet, resnet, inception, mobilenet)
Pytorch implements bird recognition (including training code and bird data set) Table of Contents Pytorch implements bird recognition (including training code and bird data set) 1 Introduction 2. Bird Dataset (1)Bird-Dataset26 (2) Custom data set 3. Bird classification and recognition model training (1) Project installation (2) Prepare Train and Test data (3) Configuration file: ?config.yaml? […]
ResNet classifier quantification
1. Commonly used calculation methods for dynamic range Max method The previous symmetric quantization and asymmetric quantization were done using the maximum and minimum method. Histgram A histogram is a graphical tool used to visualize the distribution of a signal or data. One way to calculate dynamic range is by looking at the range of […]
resnet image classification code
Directory Structure: import torch import torchvision import torchvision.models import os from matplotlib import pyplot as plt from tqdm import tqdm from torch import nn import tensorflow astf import pathlib import PIL from sklearn.metrics import accuracy_score from torch.utils.data import DataLoader from torchvision.transforms import transforms from sklearn.metrics import accuracy_score, confusion_matrix, classification_report # data_transform = { # “train”: […]
7.resnet50 network implementation
code show as below: import torch.nn as nn import torch # Resnet 18/34 uses this residual block class BasicBlock(nn.Module): # Convolution 2 layers, the dimensions of F(X) and X are equal # expansion is the multiple of F(X) relative to the X dimension expansion expansion = 1 # Whether the dimensions of the residual mapping […]
Fruit classification and recognition system based on deep residual network (ResNet)
Tip: After the article is written, the table of contents can be automatically generated. For how to generate it, please refer to the help document on the right. Article directory Preface 1. Background meaning project description 2. Data preprocessing 3. Network structure 1. Use residual networks (ResNets) 4. Loss function 5. Detailed description of the […]
Resnet20 code review
Because a VGG code found on github is not compatible with the resnet training code of the original project (the training results do not converge). I didn’t think of any other shortcuts to solve the problem, so let’s take a look at the code. Initialization of class class resnet20(nn.Module): def __init__(self, num_class): super(resnet20, self).__init__() self.conv1 […]
Image classification training using Resnet
This article only gives the most basic baseline for image classification training. The model structure can be modified based on this code later. 1. Image classification data set There is an image category data set with categories Y and N. The data directory is as follows: /datasets/data/ |– train/ | |– Y/ | |– N/ […]
Classic network analysis (4) ResNet | Residual module, network structure code realizes full analysis
Article directory 1 Original design intention 2.Network structure 2.1 Residual block 2.2 Intermediate convolutional network feature extraction block 1 Two layers of 3×3 convolutional layers 2 First 1×1 convolution layer, then 3×3 convolution layer, then 3×3 convolution layer 2.3 Structure overview table 3 Why does the residual module work? 3.1 Forward propagation 3.2 Backpropagation 3.3 […]