diff --git a/examples/dataset_image_workflow/benchmarks/dataset/data.py b/examples/dataset_image_workflow/benchmarks/dataset/data.py
index 39dcc7e..81ed6e5 100644
--- a/examples/dataset_image_workflow/benchmarks/dataset/data.py
+++ b/examples/dataset_image_workflow/benchmarks/dataset/data.py
@@ -8,7 +8,7 @@ from torchvision.transforms import transforms
 from torch.utils.data import TensorDataset
 
 from .utils import cached
-from examples.dataset_cifar_workflow.benchmarks.dataset.utils import split_dataset, build_transforms
+from examples.dataset_image_workflow.benchmarks.dataset.utils import split_dataset, build_transforms
 
 cache_root = os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..', '..', 'cache'))
 
diff --git a/examples/dataset_image_workflow/main.py b/examples/dataset_image_workflow/main.py
index 9d09fe4..ec2d81b 100644
--- a/examples/dataset_image_workflow/main.py
+++ b/examples/dataset_image_workflow/main.py
@@ -29,7 +29,7 @@ class ImageDatasetWorkflow:
         learnware.init()
         assert not rebuild
 
-        market_id = "dataset_cifar_workflow" if market_id is None else market_id
+        market_id = "dataset_image_workflow" if market_id is None else market_id
         orders = np.stack([np.random.permutation(10) for _ in range(market_size)])
 
         print("Using market_id", market_id)
@@ -48,7 +48,7 @@ class ImageDatasetWorkflow:
     def evaluate(self, user_size=100, market_id=None, faster=True):
         learnware.init()
 
-        market_id = "dataset_cifar_workflow" if market_id is None else market_id
+        market_id = "dataset_image_workflow" if market_id is None else market_id
         orders = np.stack([np.random.permutation(10) for _ in range(user_size)])
 
         print("Using market_id", market_id)
diff --git a/examples/dataset_image_workflow/mock.py b/examples/dataset_image_workflow/mock.py
deleted file mode 100644
index e08199b..0000000
--- a/examples/dataset_image_workflow/mock.py
+++ /dev/null
@@ -1,83 +0,0 @@
-import os.path
-import random
-
-import numpy as np
-import torch
-from torch.utils.data import DataLoader, TensorDataset
-from torchvision import datasets
-from torchvision.transforms import transforms
-
-import learnware
-from examples.dataset_cifar_workflow.benchmarks.dataset import user_data, split_dataset
-from examples.dataset_image_workflow.get_data import get_zca_matrix, transform_data
-from learnware import setup_seed
-from learnware.specification import generate_rkme_image_spec, RKMEImageSpecification
-
-
-def f(d):
-    return np.exp(-d / 0.00005)
-
-def get_spec(path, order=None):
-    if path is not None and os.path.exists(path):
-        spec = RKMEImageSpecification()
-        spec.load(path)
-        return spec, spec.msg
-
-    test_user, spec_user, _, order = user_data(order=order)
-    loader = DataLoader(spec_user, batch_size=3000, shuffle=True)
-    sampled_X, _ = next(iter(loader))
-    spec = generate_rkme_image_spec(sampled_X, whitening=False)
-    spec.msg = order
-
-    if path is not None:
-        spec.save(path)
-
-    return spec, order
-
-DATA_ROOT = "cache"
-def get_cifar10(output_channels=3, image_size=32, z_score=True, order=None):
-    ds_train = datasets.CIFAR10(DATA_ROOT, train=True, download=True, transform=transforms.Compose(
-        [transforms.ToTensor(), transforms.Resize([image_size, image_size])]))
-    X_train = ds_train.data
-    y_train = ds_train.targets
-    ds_test = datasets.CIFAR10(DATA_ROOT, train=False, download=True, transform=transforms.Compose(
-        [transforms.ToTensor(), transforms.Resize([image_size, image_size])]))
-
-    X_test = ds_test.data
-    y_test = ds_test.targets
-
-    X_train = torch.Tensor(np.moveaxis(X_train, 3, 1))
-    y_train = torch.Tensor(y_train).long()
-    X_test = torch.Tensor(np.moveaxis(X_test, 3, 1))
-    y_test = torch.Tensor(y_test).long()
-
-    if output_channels == 1:
-        X_train = torch.mean(X_train, 1, keepdim=True)
-        X_test = torch.mean(X_test, 1, keepdim=True)
-
-    if z_score:
-        X_test = (X_test - torch.mean(X_train, [0, 2, 3], keepdim=True)) / (torch.std(X_train, [0, 2, 3], keepdim=True))
-        X_train = (X_train - torch.mean(X_train, [0, 2, 3], keepdim=True)) / (
-            torch.std(X_train, [0, 2, 3], keepdim=True))
-
-    whitening_mat = get_zca_matrix(X_train, reg_coef=0.1)
-    train_X = transform_data(X_train, whitening_mat)
-    test_X = transform_data(X_train, whitening_mat)
-
-    selected_data_indexes, order = split_dataset(y_test, 10000, split="user", order=order)
-
-    return TensorDataset(test_X[selected_data_indexes], y_test[selected_data_indexes]), order
-
-
-
-
-if __name__ == "__main__":
-    # old1, order1 = get_spec("spec_1_V100.json", order=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
-    # old2, order2 = get_spec("spec_2_A100.json", order=[2, 3, 4, 5, 6, 7, 0, 1, 8, 9])
-
-    old3, order3 = get_spec("spec_3_A100.json", order=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
-    old4, order4 = get_spec("spec_6_A100.json", order=[2, 3, 4, 5, 6, 7, 0, 1, 8, 9])
-
-    print(order3, order4)
-    print(f(old3.dist(old4)))
-