---
task_categories:
- image-classification
size_categories:
- 100K<n<1M
---

[CINIC10](https://github.com/BayesWatch/cinic-10) dataset with interface of [CIFAR10](https://github.com/pytorch/vision/blob/main/torchvision/datasets/cifar.py).

It is faster than the common CINIC10 due to the fact that all images are loaded into RAM while initing dataset instance.


You should save `cinic10.py` from this repo in local directory. And then import the CINIC10 class from it:

```
import torchvision
import torch
from torchvision transforms
from cinic10 import CINIC10

data_mean = [0.47889522, 0.47227842, 0.43047404]
data_std = [0.24205776, 0.23828046, 0.25874835]

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 = CINIC10(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 = CINIC10(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)

```