Hello everyone, I am Wei Xue AI. Today I will introduce to you the artificial intelligence (pytorch) building model 22 - building a SimpleBaseline (human body key point detection) model based on pytorch, and introduce the network model and code implementation in detail. This article will introduce the principles of the SimpleBaseline model, use the pytorch framework to build the model, and its application scenarios. The SimpleBaseline model is a deep learning model for human body key point detection. It can be used to detect key points in human posture, such as head, shoulders, elbows, wrists, hips, knees and ankles. The SimpleBaseline model combines the characteristics of convolutional neural network (CNN) and residual network (ResNet) to achieve accurate positioning of key points of human posture through end-to-end learning. This model has been widely used in the field of computer vision, for example, it is of great significance in human action recognition, sports analysis, medical image analysis, etc.
Table of contents
- introduction
- Overview of the SimpleBaseline model
- The structure and principle of the SimpleBaseline model
- pytorch builds SimpleBaseline model
- Application scenarios of SimpleBaseline model
- in conclusion
1 Introduction
In recent years, with the development of deep learning and the advancement of computer vision technology, human body key point detection has become an important research direction in the field of computer vision. Human body key point detection aims to accurately identify and locate key points of the human body from images or videos, such as head, shoulders, elbows, wrists, hips, knees and ankles, etc.
It is a deep learning model for human key point detection, which is known for its simple and efficient architecture. The design idea of this model is to achieve accurate positioning of key points of human posture by utilizing the structural characteristics of convolutional neural network (CNN) and residual network (ResNet).
2. Overview of SimpleBaseline model
SimpleBaseline model is a model for human body key point detection, which was proposed by the research team of Peking University. This model has gained a lot of attention for its simple design and excellent performance. It is based on deep learning technology and uses convolutional neural network (CNN) as the basic structure to identify and predict key points of the human body.
The core structure of the SimpleBaseline model includes a backbone network and a keypoint regressor. The backbone network is responsible for extracting useful feature representations from the input image and passing these features to the keypoint regressor for keypoint location. The key point regressor maps the features extracted by the backbone network to key point locations in the real world by learning a mapping function. The training process of this model usually adopts the supervised learning method, that is, by providing the marked positions of key points of the human body as training data, and using the loss function to optimize the model parameters, so that it can accurately predict the positions of the key points of the human body. This model has several advantages. First, its network structure is simple and efficient, and does not require excessively complex design and computing resources. Secondly, the model can show good performance in various complex environments and has strong generalization ability. In addition, the SimpleBaseline model achieves a good balance between speed and accuracy and is suitable for real-time applications and large-scale data processing.
3. Structure and principle of SimpleBaseline model
The core of the SimpleBaseline model is a convolutional neural network based on the residual network (ResNet). The ResNet-based design allows information to be effectively transferred between layers of the network, thus facilitating the training of deep networks.
First, the image is passed through ResNet to generate a series of feature maps. These feature maps are then processed through three consecutive convolutional layers and upsampling layers to generate finer feature maps. Finally, a 1x1 convolutional layer is used to convert the feature map into a keypoint heat map.
The model training process uses the mean square error loss function, which compares the difference between the predicted key point heat map and the real key point heat map to optimize the parameters of the model.
SimpleBaseline is an image key point detection model based on convolutional neural network (CNN). It is divided into the following parts:
1. CNN feature extraction: SimpleBaseline uses a pre-trained ResNet as a feature extractor to convert the input image into a high-dimensional feature vector through operations such as convolution and pooling.
2.Hourglass module: The Hourglass module is the core part of SimpleBaseline and is used for multi-level processing and fusion of features to improve the accuracy of key point detection. The Hourglass module consists of multiple repeated down-sampling and up-sampling steps, where operations such as pooling and convolution are used in the down-sampling process, while techniques such as deconvolution and residual connections are used in the up-sampling process.
3. Key point prediction: After processing by the Hourglass module, SimpleBaseline uses a small convolutional neural network to perform regression prediction for each key point. This sub-network contains multiple convolutional and fully connected layers, and its output is the coordinate position of the key point.
The principle of the SimpleBaseline model can be summarized as using a convolutional neural network for feature extraction, combined with the Hourglass module for multi-level processing and fusion, and finally using a sub-network to perform regression predictions on key points.
4. Build SimpleBaseline model with pytorch
import torch
import torch.nn as nn
import torchvision
class ResBlock(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(ResBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1,bias=False)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.relu(self.bn1(self.conv1(x)))
out = self.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
if self.downsample is not None:
residual = self.downsample(x)
out += residual
return self.relu(out)
class SimpleBaseline(nn.Module):
def __init__(self, nJoints):
super(SimpleBaseline, self).__init__()
self.inplanes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(ResBlock, 64, 3)
self.layer2 = self._make_layer(ResBlock, 128, 4, stride=2)
self.layer3 = self._make_layer(ResBlock, 256, 6, stride=2)
self.layer4 = self._make_layer(ResBlock, 512, 3, stride=2)
self.deconv_layers = self._make_deconv_layer()
self.final_layer = nn.Conv2d(in_channels=256,out_channels=nJoints,kernel_size=1,stride=1,padding=0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def _make_deconv_layer(self):
layers = []
for i in range(3):
layers.append(nn.ConvTranspose2d(in_channels=self.inplanes,out_channels=256,kernel_size=4,
stride=2,padding=1,output_padding=0,bias=False))
layers.append(nn.BatchNorm2d(256))
layers.append(nn.ReLU(inplace=True))
self.inplanes = 256
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.deconv_layers(x)
x = self.final_layer(x)
return x
if __name__ == '__main__':
model = SimpleBaseline(nJoints=16)
print(model)
data = torch.randn(1,3,256,192)
out = model(data)
print(out.shape)
5. Application scenarios of SimpleBaseline model
The SimpleBaseline model is very versatile and can be used to detect human body key points in a variety of applications.
1.Sports Analysis: In sports competitions, the SimpleBaseline model can be used to track the movements of athletes for more in-depth sports analysis. .
2.Health Monitoring: In the field of healthcare, people’s health status can be assessed by analyzing their actions.
3.Games and Entertainment: In video games and augmented reality applications, this model can be used to capture the player's Dynamic, providing a more immersive experience.
4.Safety monitoring: In safety monitoring, abnormal behaviors can be detected by analyzing the behaviors and actions of pedestrians. detection.
5 Conclusion
SimpleBaseline model is a powerful and easy-to-implement human key point detection model. It uses deep learning technology to achieve accurate and efficient human key point detection in various application scenarios through a simple and effective method. Despite the model's relatively simple structure, its performance is comparable to state-of-the-art models, which fully demonstrates the superiority of its design.
I hope you can gain an in-depth understanding of the SimpleBaseline model from this article and find its value in your research or application.