PyTorch Vision (torchvision)

ImageFolder + DataLoader

from torchvision.datasets import ImageFolder
from torchvision import transforms
from torch.utils.data import DataLoader

tf = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
ds = ImageFolder("data/train", transform=tf)
loader = DataLoader(ds, batch_size=32, shuffle=True, num_workers=4)

Directory layout: one subfolder per class name; ds.classes lists labels.

Models and Weights

import torch
from torchvision.models import resnet50, ResNet50_Weights

weights = ResNet50_Weights.IMAGENET1K_V2
model = resnet50(weights=weights).eval()
preprocess = weights.transforms()

# x: batch from preprocess(PIL) or equivalent
with torch.no_grad():
    logits = model(x)

Transforms v2 (brief)

Newer APIs under torchvision.transforms.v2 accept tensors or PIL and support bbox/mask transforms consistently—prefer them for detection/segmentation training when your version includes them.