1134_252_159.py

!pip install torch torchvision numpy opencv-python

from torchvision import datasets, transforms

transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])

# Insert your 'path_to_train_data'
train_dataset = datasets.ImageFolder(root='path_to_train_data', transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=32, shuffle=True)

import torch.nn as nn
import torchvision.models as models

model = models.resnet18(pretrained=True)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, num_classes)  # num_classes should be the number of individuals in your dataset

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

for epoch in range(num_epochs):
    for inputs, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

model.eval()
correct = 0
total = 0
with torch.no_grad():
    for inputs, labels in test_loader:
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

accuracy = 100 * correct / total
print(f'Accuracy: {accuracy}%')

model.eval()
img = Image.open('path_to_image')
img = transform(img).unsqueeze(0)  # Add batch dimension
output = model(img)
_, predicted = torch.max(output, 1)
print(f'Predicted Class: {predicted.item()}')