diff --git a/python/paddle/v2/dataset/cifar.py b/python/paddle/v2/dataset/cifar.py
new file mode 100644
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+++ b/python/paddle/v2/dataset/cifar.py
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+"""
+CIFAR Dataset.
+
+URL: https://www.cs.toronto.edu/~kriz/cifar.html
+
+the default train_creator, test_creator used for CIFAR-10 dataset.
+"""
+from config import DATA_HOME
+import os
+import hashlib
+import urllib2
+import shutil
+import tarfile
+import cPickle
+import itertools
+import numpy
+
+__all__ = [
+    'cifar_100_train_creator', 'cifar_100_test_creator', 'train_creator',
+    'test_creator'
+]
+
+CIFAR10_URL = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
+CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a'
+CIFAR100_URL = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
+CIFAR100_MD5 = 'eb9058c3a382ffc7106e4002c42a8d85'
+
+
+def __read_batch__(filename, sub_name):
+    def reader():
+        def __read_one_batch_impl__(batch):
+            data = batch['data']
+            labels = batch.get('labels', batch.get('fine_labels', None))
+            assert labels is not None
+            for sample, label in itertools.izip(data, labels):
+                yield (sample / 255.0).astype(numpy.float32), int(label)
+
+        with tarfile.open(filename, mode='r') as f:
+            names = (each_item.name for each_item in f
+                     if sub_name in each_item.name)
+
+            for name in names:
+                batch = cPickle.load(f.extractfile(name))
+                for item in __read_one_batch_impl__(batch):
+                    yield item
+
+    return reader
+
+
+def download(url, md5):
+    filename = os.path.split(url)[-1]
+    assert DATA_HOME is not None
+    filepath = os.path.join(DATA_HOME, md5)
+    if not os.path.exists(filepath):
+        os.makedirs(filepath)
+    __full_file__ = os.path.join(filepath, filename)
+
+    def __file_ok__():
+        if not os.path.exists(__full_file__):
+            return False
+        md5_hash = hashlib.md5()
+        with open(__full_file__, 'rb') as f:
+            for chunk in iter(lambda: f.read(4096), b""):
+                md5_hash.update(chunk)
+
+        return md5_hash.hexdigest() == md5
+
+    while not __file_ok__():
+        response = urllib2.urlopen(url)
+        with open(__full_file__, mode='wb') as of:
+            shutil.copyfileobj(fsrc=response, fdst=of)
+    return __full_file__
+
+
+def cifar_100_train_creator():
+    fn = download(url=CIFAR100_URL, md5=CIFAR100_MD5)
+    return __read_batch__(fn, 'train')
+
+
+def cifar_100_test_creator():
+    fn = download(url=CIFAR100_URL, md5=CIFAR100_MD5)
+    return __read_batch__(fn, 'test')
+
+
+def train_creator():
+    """
+    Default train reader creator. Use CIFAR-10 dataset.
+    """
+    fn = download(url=CIFAR10_URL, md5=CIFAR10_MD5)
+    return __read_batch__(fn, 'data_batch')
+
+
+def test_creator():
+    """
+    Default test reader creator. Use CIFAR-10 dataset.
+    """
+    fn = download(url=CIFAR10_URL, md5=CIFAR10_MD5)
+    return __read_batch__(fn, 'test_batch')
+
+
+def unittest():
+    for _ in train_creator()():
+        pass
+    for _ in test_creator()():
+        pass
+
+
+if __name__ == '__main__':
+    unittest()