Update README.md

README.md

@@ -7,9 +7,41 @@ size_categories:
 
 [CINIC10](https://github.com/BayesWatch/cinic-10) dataset with interface of [CIFAR10](https://github.com/pytorch/vision/blob/main/torchvision/datasets/cifar.py).
 
-**CINIC10**
+It is faster than the common CINIC10 due to the fact that all images are loaded into RAM while initing dataset instance.
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/6603fa9907e0b8a75b819c61/LcEiSYmYFtC3D2wpG2uuB.png)
 
 
-It is faster than the common CINIC10 due to the fact that all images are loaded into RAM while initing dataset instance.
+The simplest way to use CINIC-10 is with a PyTorch data loader, as follows:
+
+```
+import torchvision
+import torch
+from torchvision transforms
+from cinic10 import CINIC10
+
+transform_train = transforms.Compose([
+  transforms.RandomCrop(32, padding=4),
+  transforms.Resize(32),
+  transforms.RandomHorizontalFlip(),
+  transforms.ToTensor(),
+  transforms.Normalize(data_mean, data_std),
+])
+
+transform_test = transforms.Compose([
+    transforms.Resize(32),
+    transforms.ToTensor(),
+    transforms.Normalize(data_mean, data_std),
+])
+
+batch_size = 64
+num_workers = 4
+
+trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
+trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+
+testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
+testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=num_workers)
+
+```
+
+