Artificial intelligence (pytorch) builds a model 17-pytorch builds a ReitnNet model, loads data for model training and prediction

Hello everyone, I am Weixue AI. Today I will introduce to you artificial intelligence (pytorch) to build a model 17-pytorch to build a ReitnNet model, load data for model training and prediction, and RetinaNet is a deep learning model for target detection tasks. It aims to solve the problem of hard samples and imbalanced classes in object detection. It is an improved method based on a single-stage detector, which achieves efficient and accurate object detection by introducing a specific loss function and network structure.

The core innovation of RetinaNet is to use a loss function called Focal Loss to deal with the problem of category imbalance during training. In target detection tasks, negative samples (i.e., non-targets) are usually much more than positive samples (i.e., targets), which will lead to the model's over-predictive ability for negative samples and weak predictive ability for positive samples. Focal Loss adjusts the weight of easy-to-classify samples to make the model pay more attention to samples that are difficult to classify, thereby increasing the attention to positive samples and improving the accuracy of target detection.

Table of contents

1. introduction
2. RetinaNet model principle
3. Sample CSV data
4. data loading
5. Using the PyTorch framework to train and predict the RetinaNet model
6. in conclusion

1 Introduction

In the field of deep learning, object detection is an important research direction. RetinaNet is an efficient target detection model, which solves the problem of foreground and background category imbalance by introducing Focal Loss, thus achieving remarkable results in target detection tasks. This article will introduce the principle of the RetinaNet model in detail, and show how to use the PyTorch framework to train and predict the RetinaNet model through a practical project.

2. RetinaNet model principle

RetinaNet is a deep learning-based object detection model, which consists of two parts: Feature Pyramid Network (FPN) and classification/regression sub-network. The FPN is used to extract features from the input image, while the classification/regression sub-network is used to predict the category and location of the object.

The key innovation of RetinaNet is the introduction of a new loss function - Focal Loss. In traditional object detection models, since the number of samples of the background category is much larger than that of the foreground category, the model is often dominated by a large number of background samples, resulting in a decrease in the detection performance of the foreground category. Focal Loss solves this problem by giving more weight to samples that are difficult to classify.

RetinaNet is a target detection model based on deep learning, and its mathematical principle can be expressed by the following formula:

First, for an input image, feature maps are extracted using a basic convolutional neural network such as ResNet. Suppose the size of the feature map is $H\times W\times C$ , where$HwaWW$ _$W$ stands for height and width, respectively, and C stands for the number of channels.

Then, RetinaNet introduces a Feature Pyramid Network (FPN) to handle objects of different sizes by generating feature maps with different scales at different levels. The feature map of each level in FPN can be expressed as $P_i$, where i represents the index of the hierarchy. Each $P_i$The size is $H_i\times W_i\times C_i$。

Next, RetinaNet introduces two parallel subnetworks: object classification subnetwork and bounding box regression subnetwork.

The object classification subnetwork divides each $P_i$The feature map of is mapped to a feature map with K channels, where $K$ denotes the number of target categories (including background). This feature map represents the probability that each pixel belongs to a different class. Then, these probabilities are normalized using the softmax function to obtain the final classification probabilities.

The bounding box regression subnetwork divides each $P_i$The feature map of is mapped to a feature map with 4 channels. This feature map represents the coordinate regression prediction of each pixel corresponding to the object bounding box.

3. Sample CSV data

The following are some CSV data samples, each row of data contains the path of the image, the coordinates and category of the target:

/path/to/image1.jpg,100,120,200,230,cat
/path/to/image1.jpg,300,400,500,600,dog
/path/to/image2.jpg,50,100,150,200,bird
/path/to/image3.jpg,100,120,200,230,cat
/path/to/image4.jpg,300,400,500,600,dog
/path/to/image5.jpg,50,100,150,200,bird
...

4. Data loading

We first need to load the CSV data and convert it into a format that the model can accept. Here is the code for data loading:

import csv
import torch
from PIL import Image

class CSVDataset(torch.utils.data.Dataset):
    def __init__(self, csv_file):
        self.data = []
        with open(csv_file, 'r') as f:
            reader = csv.reader(f)
            for row in reader:
                img_path, x1, y1, x2, y2, class_name = row
                self.data.append((img_path, (x1, y1, x2, y2), class_name))

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        img_path, bbox, class_name = self.data[idx]
        img = Image.open(img_path).convert('RGB')
        return img, bbox, class_name

5. Using the PyTorch framework to train and predict the RetinaNet model

Next, we will use the PyTorch framework to train and predict the RetinaNet model. Here is the code for training and prediction:

import torch
from torch import nn
from torch.optim import Adam
from torchvision.models.detection import retinanet_resnet50_fpn

# 加载数据
dataset = CSVDataset('data.csv')
data_loader = torch.utils.data.DataLoader(dataset, batch_size=2, shuffle=True)

# 创建模型
model = retinanet_resnet50_fpn(pretrained=True)
model = model.cuda()

# 定义优化器和损失函数
optimizer = Adam(model.parameters(), lr=1e-4)
criterion = nn.CrossEntropyLoss()

# 训练模型
for epoch in range(10):
    for imgs, bboxes, class_names in data_loader:
        imgs = imgs.cuda()
        bboxes = bboxes.cuda()
        class_names = class_names.cuda()
        # 前向传播
        outputs = model(imgs)
        # 计算损失
        loss = criterion(outputs, class_names)
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, loss.item()))

# 预测
model.eval()
with torch.no_grad():
    for imgs, _, _ in data_loader:
        imgs = imgs.cuda()
        outputs = model(imgs)
        print(outputs)

6 Conclusion

This article introduces the principle of the RetinaNet model in detail, and shows how to use the PyTorch framework to train and predict the RetinaNet model through a practical project. The RetinaNet model solves the problem of unbalanced foreground and background categories by introducing Focal Loss, thus achieving remarkable results in target detection tasks. Hope this article can be helpful to your study and research.