diff --git a/milabench/datasets/__init__.py b/milabench/datasets/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/milabench/datasets/fake_images.py b/milabench/datasets/fake_images.py
deleted file mode 100644
index 6e7c14de6..000000000
--- a/milabench/datasets/fake_images.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import multiprocessing
-import os
-
-from torchvision.datasets import FakeData
-from tqdm import tqdm
-
-from ..fs import XPath
-
-
-def write(args):
-    image_size, offset, count, outdir = args
-    dataset = FakeData(
-        size=count, image_size=image_size, num_classes=1000, random_offset=offset
-    )
-
-    image, y = next(iter(dataset))
-    class_val = int(y)
-    image_name = f"{offset}.jpeg"
-
-    path = os.path.join(outdir, str(class_val))
-    os.makedirs(path, exist_ok=True)
-
-    image_path = os.path.join(path, image_name)
-    image.save(image_path)
-
-
-def generate(image_size, n, outdir):
-    p_count = min(multiprocessing.cpu_count(), 8)
-    pool = multiprocessing.Pool(p_count)
-    for _ in tqdm(
-        pool.imap_unordered(write, ((image_size, i, n, outdir) for i in range(n))),
-        total=n,
-    ):
-        pass
-
-
-def generate_sets(root, sets, shape):
-    root = XPath(root)
-    sentinel = root / "done"
-    if sentinel.exists():
-        print(f"{root} was already generated")
-        return
-    if root.exists():
-        print(f"{root} exists but is not marked complete; deleting")
-        root.rm()
-    for name, n in sets.items():
-        print(f"Generating {name}")
-        generate(shape, n, os.path.join(root, name))
-    sentinel.touch()