Image Recognition Introduction - ( Image Recognition with PyTorch )

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Syntax

PyTorch

import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

Example

PyTorch

import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

net = Net()

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

for epoch in range(2):

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 2000 == 1999:   
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

Output

PyTorch

Files already downloaded and verified
Files already downloaded and verified
[1,  2000] loss: 2.226
[1,  4000] loss: 1.849
[1,  6000] loss: 1.686
[1,  8000] loss: 1.582
[1, 10000] loss: 1.497
[1, 12000] loss: 1.473
[2,  2000] loss: 1.395
[2,  4000] loss: 1.350
[2,  6000] loss: 1.342
[2,  8000] loss: 1.318
[2, 10000] loss: 1.287
[2, 12000] loss: 1.273

Explanation

Image recognition is the process of identifying and detecting objects or patterns in images or videos. With emerging technologies like deep learning, it has become easier to build state-of-the-art image recognition systems.

PyTorch is one such framework that provides a simple and efficient way to create deep learning models for image recognition.

In this example, we create a convolutional neural network (CNN) using PyTorch to identify objects in CIFAR-10 images dataset.

Use

Image recognition has a wide range of applications, from facial recognition to self-driving cars. It can be used in various industries like healthcare, security, and entertainment.

PyTorch provides a flexible and efficient way to build deep learning models that can recognize complex objects and patterns in images and videos.

Important Points

• Image recognition is the process of identifying and detecting objects or patterns in images or videos
• PyTorch is a flexible and efficient framework for building deep learning models for image recognition
• Convolutional neural networks (CNN) are commonly used for image recognition tasks
• PyTorch provides a simple and efficient way to build CNN models

Summary

In summary, PyTorch is a powerful framework for building image recognition systems using deep learning techniques. By using PyTorch, you can create models that can detect and identify objects or patterns in images or videos, which has various applications in industries such as healthcare, security, and entertainment.