You can use the following code for creating the train val split. You can specify the val_split float value (between 0.0 to 1.0) in the train_val_dataset function. You can modify the function and also create a train test val split if you want by splitting the indices of list(range(len(dataset))) in three subsets. Just remember to shuffle the list before splitting else you won’t get all the classes in the three splits since these indices would be used by the Subset class to sample from the original dataset.
import torch
from torchvision.datasets import ImageFolder
from torch.utils.data import Subset
from sklearn.model_selection import train_test_split
from torchvision.transforms import Compose, ToTensor, Resize
from torch.utils.data import DataLoader

def train_val_dataset(dataset, val_split=0.25):
    train_idx, val_idx = train_test_split(list(range(len(dataset))), test_size=val_split)
    datasets = {}
    datasets['train'] = Subset(dataset, train_idx)
    datasets['val'] = Subset(dataset, val_idx)
    return datasets

dataset = ImageFolder('C:\Datasets\lcms-dataset', transform=Compose([Resize((224,224)),ToTensor()]))
print(len(dataset))
datasets = train_val_dataset(dataset)
print(len(datasets['train']))
print(len(datasets['val']))
# The original dataset is available in the Subset class
print(datasets['train'].dataset)

dataloaders = {x:DataLoader(datasets[x],32, shuffle=True, num_workers=4) for x in ['train','val']}
x,y = next(iter(dataloaders['train']))
print(x.shape, y.shape)

50080
37560
12520
Dataset ImageFolder
    Number of datapoints: 50080
    Root location: C:\Datasets\some-dataset
    StandardTransform
Transform: Compose(
               Resize(size=(224, 224), interpolation=PIL.Image.BILINEAR)
               ToTensor()
           )
torch.Size([32, 3, 224, 224]) torch.Size([32])