0import albumentations as A from albumentations.pytorch import ToTensorV2 import cv2 import numpy as np from torch.utils.data import Dataset, DataLoader import torch import torch.nn as nn import torch.optim as optim # Define Albumentations augmentations for training train_transform = A.Compose([ A.HorizontalFlip(p=0.5), A.RandomBrightnessContrast(p=0.2), A.Rotate(limit=15, p=0.3), A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), ToTensorV2() ]) # Validation transform: only normalization val_transform = A.Compose([ A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), ToTensorV2() ]) # Custom Dataset using Albumentations class CustomImageDataset(Dataset): def __init__(self, image_paths, labels, transform=None): self.image_paths = image_paths self.labels = labels self.transform = transform def __len__(self): return len(self.image_paths) def __getitem__(self, idx): image = cv2.imread(self.image_paths[idx]) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) label = self.labels[idx] if self.transform: augmented = self.transform(image=image) image = augmented['image'] return image, label # Dummy data placeholders (replace with real data paths and labels) train_image_paths = ['train_img1.jpg', 'train_img2.jpg'] train_labels = [0, 1] val_image_paths = ['val_img1.jpg', 'val_img2.jpg'] val_labels = [0, 1] train_dataset = CustomImageDataset(train_image_paths, train_labels, transform=train_transform) val_dataset = CustomImageDataset(val_image_paths, val_labels, transform=val_transform) train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True) val_loader = DataLoader(val_dataset, batch_size=32) # Simple model class SimpleCNN(nn.Module): def __init__(self): super().__init__() self.conv = nn.Sequential( nn.Conv2d(3, 16, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2), nn.Conv2d(16, 32, 3, padding=1), nn.ReLU(), nn.MaxPool2d(2) ) self.fc = nn.Linear(32 * 56 * 56, 2) # assuming input images 224x224 def forward(self, x): x = self.conv(x) x = x.view(x.size(0), -1) x = self.fc(x) return x # Training setup device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = SimpleCNN().to(device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # Training loop for epoch in range(5): model.train() train_loss = 0 correct = 0 total = 0 for images, labels in train_loader: images, labels = images.to(device), labels.to(device) optimizer.zero_grad() outputs = model(images) loss = criterion(outputs, labels) loss.backward() optimizer.step() train_loss += loss.item() _, predicted = outputs.max(1) total += labels.size(0) correct += predicted.eq(labels).sum().item() train_acc = 100 * correct / total model.eval() val_loss = 0 correct = 0 total = 0 with torch.no_grad(): for images, labels in val_loader: images, labels = images.to(device), labels.to(device) outputs = model(images) loss = criterion(outputs, labels) val_loss += loss.item() _, predicted = outputs.max(1) total += labels.size(0) correct += predicted.eq(labels).sum().item() val_acc = 100 * correct / total print(f'Epoch {epoch+1}: Train Loss {train_loss/len(train_loader):.3f}, Train Acc {train_acc:.2f}%, Val Loss {val_loss/len(val_loader):.3f}, Val Acc {val_acc:.2f}%')
Before augmentation: Training accuracy 95%, Validation accuracy 75%, Validation loss 0.85
After augmentation: Training accuracy 90%, Validation accuracy 87%, Validation loss 0.65
import albumentations as A transform = A.Compose([A.Resize(128, 128)]) augmented = transform(image=image) result = augmented['image'].shape
import albumentations as A
transform = A.Compose([
A.HorizontalFlip(p=0.5),
A.RandomBrightnessContrast(p=1.2)
])
augmented = transform(image=image)Pass the test and this pattern moves unknown strong while earning +1 Grip toward your certificate.