diff --git a/datasig/dataset.py b/datasig/dataset.py
index 8f34daf..ac04b13 100644
--- a/datasig/dataset.py
+++ b/datasig/dataset.py
@@ -151,6 +151,108 @@ def deserialize_data_point(data: bytes) -> bytes:
         return data
 
 
+class TensorFlowDataset(SequenceDataset):
+    """Dataset loaded using the tensorflow_datasets python API"""
+
+    def __init__(self, dataset: Sequence[dict[str, Any]]):
+        """
+        Initialize a TensorFlow dataset.
+
+        Args:
+            dataset: A tensorflow_datasets dataset that supports indexing and length.
+                    Each sample should be a dictionary with keys like 'image' and 'label'.
+        """
+        self.dataset = dataset
+
+    def __getitem__(self, idx: int) -> bytes:
+        return TensorFlowDataset.serialize_data_point(self.dataset[idx])
+
+    def __len__(self) -> int:
+        return len(self.dataset)
+
+    def __iter__(self) -> Iterator[bytes]:
+        return (
+            TensorFlowDataset.serialize_data_point(data_point) for data_point in iter(self.dataset)
+        )
+
+    @property
+    def name(self) -> str:
+        """Name identifying the dataset"""
+        # TensorFlow datasets have a name attribute
+        if hasattr(self.dataset, "name"):
+            return self.dataset.name
+        return "TensorFlowDataset"
+
+    @staticmethod
+    def serialize_data_point(data: dict[str, Any]) -> bytes:
+        """
+        Serialize a tensorflow dataset sample.
+
+        Expected format: {'image': array, 'label': int}
+        Serializes as: [label (4 bytes)][image_shape_len (4 bytes)][image_shape][image_dtype_len (4 bytes)][image_dtype][image_data]
+        """
+        # Handle both dict format and tuple format (image, label)
+        if isinstance(data, dict):
+            image = data["image"]
+            label = int(data["label"])
+        else:
+            # Assume it's a tuple/list (image, label)
+            image, label = data
+            label = int(label)
+
+        # Convert image to numpy array if needed
+        import numpy as np
+        if not isinstance(image, np.ndarray):
+            image = np.array(image)
+
+        # Serialize the data
+        image_bytes = image.tobytes()
+        shape_bytes = str(image.shape).encode()
+        dtype_bytes = str(image.dtype).encode()
+
+        # Format: [label][shape_len][dtype_len][shape_bytes][image_len][dtype_bytes][image_bytes]
+        # Use '=' to disable automatic padding alignment
+        return struct.pack(
+            f"=III{len(shape_bytes)}sI{len(dtype_bytes)}s{len(image_bytes)}s",
+            label,
+            len(shape_bytes),
+            len(dtype_bytes),
+            shape_bytes,
+            len(image_bytes),
+            dtype_bytes,
+            image_bytes,
+        )
+
+    @staticmethod
+    def deserialize_data_point(data: bytes) -> dict[str, Any]:
+        """
+        Deserialize a tensorflow dataset sample back to dict format.
+        """
+        import numpy as np
+
+        # Unpack the header - use '=' to match serialization format
+        label, shape_len, dtype_len = struct.unpack_from("=III", data, offset=0)
+        offset = 12
+
+        shape_bytes: bytes = struct.unpack_from(f"={shape_len}s", data, offset=offset)[0]
+        offset += shape_len
+
+        image_len: int = struct.unpack_from("=I", data, offset=offset)[0]
+        offset += 4
+
+        dtype_bytes: bytes = struct.unpack_from(f"={dtype_len}s", data, offset=offset)[0]
+        offset += dtype_len
+
+        image_bytes: bytes = struct.unpack_from(f"={image_len}s", data, offset=offset)[0]
+
+        # Reconstruct the image
+        shape = eval(shape_bytes.decode())  # Safe since we created this
+        dtype = np.dtype(dtype_bytes.decode())
+        image = np.frombuffer(image_bytes, dtype=dtype).reshape(shape)
+
+        return {"image": image, "label": label}
+
+
 class CSVDataset(SequenceDataset):
     """Dataset contained in a CSV file"""
 
diff --git a/datasig/test/test_tensorflow_mnist.py b/datasig/test/test_tensorflow_mnist.py
new file mode 100644
index 0000000..21fe535
--- /dev/null
+++ b/datasig/test/test_tensorflow_mnist.py
@@ -0,0 +1,684 @@
+import tensorflow_datasets as tfds
+from datasig.dataset import TensorFlowDataset
+from datasig.algo import KeyedShaMinHash, UID
+from .utils import assert_fingerprint_similarity
+import itertools
+import numpy as np
+from pprint import pprint
+
+train_data = tfds.load("mnist", split="train", shuffle_files=True)
+test_data = tfds.load("mnist", split="test", shuffle_files=True)
+
+
+def test_mnist_v0():
+    dataset = TensorFlowDataset(test_data) # pyright: ignore[reportArgumentType]
+    fingerprint = KeyedShaMinHash(dataset).digest()
+    uid = UID(dataset).digest()
+    # Test UID and fingerprint
+    pprint(uid, width=70)
+    pprint(fingerprint.signature(), width=70)
+    assert (
+        uid
+        == (
+            b'\xb4B.\xb5X\n\xf4\t\xde\x89:\x01\x92o\xa7\xac\xf24\rK'
+            b'\x0c\xd1\xdd\xd1\xf1\xe2\xeep\x1d5Z\xd8'
+        )
+    )
+    assert len(fingerprint) == 400
+    assert fingerprint.signature() == (
+        b"\x00\x06\xabXrV\x03\xc4\xa9}\x1c\x9d\xb3%\x96p\xd8\xd6'F\x8a$\x8f9"
+        b'\xaaW\x02\xeb\xc4\xf1+\x0f\x00\x19\xe9\x83q\xa5\x81\x859A(\x18'
+        b'\xa9\xe52q;\xfa+\xa6Q\x85P:9\xcb\xfb\xf8\x89\xf7\xe4;'
+        b'\x00\x15\x11\xf1d\xdb\x1bg\x06R\xf7\xb2\x01\xc9\xfaU\x00\x7fK\xcf'
+        b'\xc7\xbb\x81\xea\x1cN\x8d\xd7@\x92O\x8f\x00\rO:\x9bg\xa8G'
+        b's\x89\x86\x01\xe2\x08\xe3N \xf4\xac(\rg\xaf\xb7D\xb2\x14K'
+        b'\x85\xfdag\x00\x14\x82*\xe1\xbd\x00>qEe\xe2\xdd\xefTPS\xb8\xe5\xb9'
+        b'q1\xf1\x00\x11gO\xe4d\x8a\xfd\x07\x00\x05\x15$-\\u\xb5'
+        b'\xc0\xea\xfe\x91Y\xa5\xea/\xae1\xee\xb3\x15\x8dj\xec'
+        b'\xdd\xc6\xc3\x89\xf6+YS\x00\x08\xfd d\xde\\\xed\x1bI\x03\x14'
+        b'x_\x9c \xc7\r\xa0\xeb}J\xbf\x0b\xe6s\x01\xd3\x169\\F\x00\x06\xb9H'
+        b'g\xa6 \xd3?\x1aC\xe1EKy\xf6\xa7\xdd\xeb\x84\\\x9c\x82\xcc'
+        b'\xc7\xcc\ri\x04CQ\xfe\x00\x06P\xe7\xcb,\x94*gT\x04\xfe'
+        b'\x1b$\x19\xde\xe0\xd01\x9d-\xd0zj\xfbb\xfc<\xf2\x9e1\xcd'
+        b'\x00\x02\xb5{(\xb53\xd4\n\x00\xf8\x8a\xd7\x8b\xcc\x9e\xd9`\xe7\xdf'
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+        b'?\x1b\x9f\x87\x97G\xffg\xcbG\x97\xb6\xe7\xc1U\x10\xc2e\x9a\xe5'
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+        b'\xfa\x7fn\x02\x8a<\x94\x80\xb1~\xec]\x00,=\xef6\xf9\x8f\xfe'
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+        b'\x94\xab\xc3F\xa0\xd8;\xad}\xa0\xbe\x8a\x00\x02LmN=\x06Q'
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+        b'\x00\x08T\x8c\x07k0X\x91$\x7f\xe4\xe2\xab\xe8\xddHy\xdc\xd8'
+        b'!~\x03\x97\xf9:#z\xc6D\\=\x00\x03$\x8f\x9bN=d=gS\xab2\xed\xc0\x91'
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+        b'\xc8n\x89\xe5\x00\x04\xb6+h)\x80\x08\xe8\x01\x8c\xf6'
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+        b'\x98\x9b\x8e\x15We\x16k\xf4\xf7_\xc6{\xff>\xa8?u\xeex\x00\tS\xfc'
+        b"\x98\xa9\x03\x8e\x99\xdb'\x1e\x8d\xc7\xfc\xd9!o\xfa\x0f"
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+        b'\xcd\xc0\xd7\xd2\xf6\xc6\xf6\xb4\xb2\xd4\x1d\x1f\x13\x94.\xee'
+        b':\xc1\xdf\xdb\xb2\xa2\x17\xf4\x00\x04(a%V\x1c"\xa5#\x1a\r'
+        b'+a\xbc\xaa\t\x17\x9c\xb0\xd88F\x81\x9b\xf1\xa6\x02\xe7\xe8\xe8\xf6'
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+        b'N\xe7XULh%\x0f\xff5b.je\x87f\x00\x02\xfe\x1b~\x0e\xd3\xb1'
+        b' 4\x1f\xe1=q\x04/\x00\x0fL\xf8\xc7\x1d\xf1\xe4|^xD\xf4C\xcc\xba'
+        b'\x00\x01;\x82\xc7\x02z\xfdSx\xde\xb5\xcb"X\t\x03E\xf99'
+        b'\x13\xfd\xcakG\xafa\xbdd*/\xe4\x00\x00\x15E\xf6\xdd\xb1\xdbM\xb2e]'
+        b'\x9f\xf7\x8d\x9e!\x87 o\xd8-\xadeu3\x04\xff\xf2\x91o\xbe'
+        b'\x00\t/\xaaS\xf8\xb1\xbb\xd3\xcc\x8bZ\xebk\x93\x90XAI('
+        b'\xec\xb3\xb6\x85\xdfD\xcb\x1c5v\xe8=\x00\x07\x9aZ\xcc\xb0N\x9e'
+        b'\xaaE\xbf\xa6_\x02%\xd5\xed\x1euok\\\x83\xa0\xb1\x1c\x9e\x86'
+        b'\x02{\x96%\x00\x05P\x94\xc6^\x15\x8b\xf2x^d\xe4m4\xa8\xe5\xc1\x0eW'
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+        b'\x9f\x97\xfe\x82\xf0\x8c\xfa{k\xc2\xe8\x94\x94\xd03\xbb'
+        b'\x14\x84\x99\x85\x00\x0b\x97\x11"a"\x1b*#\xec\xc45`\x80\xee'
+        b'\xd6\xe9kR\xf3"+G\xdbz\x03\xdd\x1d\x01\x98\xfe\x00\x01\xc9\x9d'
+        b'eB\x9aY\xb1\xda\xfe\xbe\x8eq\x87\xb5\x96\xb4\xf0-\x95\x9d\t\xa5'
+        b'\xbbJEF;\x1b\x8a\xbe\x00\x10)Ca\x80P\x8er\x00\xca6\xd7\x04\x00\xb9'
+        b"\x9c\x04\xa9\xe1F4\xadjyp;\x11\x9c\xdcr'\x00\n\xbc\xfe"
+        b'\xec\xde"\x9a\xbd\x04\\\xaf\x17\xb9\xab\x9c\xf3k\x03c'
+        b'\x92\x8f\xe4\xac\xb5\x83\x1f\x9bg\xc1P\x9c\x00\x0b+T\xc9sVX'
+        b'\xfa\x9a@\xa93?UC,\xbb%\xee\xcdb\ng\xb8_\n\xc8\xc3\x04\x04#'
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+        b'JgU\xe4\xb3\xc5\xb3\xac\xc8\xfc\xb4\x1b\xcd\x7f\x05\x05'
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+        b'\x00\x00\x8b\x18$u\xe4\x82$\xc7W^\xd9\xfdj\xcfD\x10\xe4\xcc'
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+        b",\xb7\x03\xed\xca\xa4\xa2\xbe{*\xd3\x0c'p\xfct\xf3\xe9\xa18"
+        b'\x0f\xd0u\xa78\xd6\xfdO'
+    )
+
+
+def test_mnist_similarity():
+    # Full training dataset
+    d0 = TensorFlowDataset(train_data) # pyright: ignore[reportArgumentType]
+    # Half training dataset
+    d1 = TensorFlowDataset(itertools.islice(train_data, 0, len(train_data) // 2)) # pyright: ignore[reportArgumentType]
+    # Merged training + test datasets
+    d2 = TensorFlowDataset(itertools.chain(train_data, test_data)) # pyright: ignore[reportArgumentType]
+    # Full test dataset
+    d3 = TensorFlowDataset(test_data) # pyright: ignore[reportArgumentType]
+
+    # Identical datasets
+    assert_fingerprint_similarity(d0, d0, 1.0)
+    # Dataset vs. half dataset
+    assert_fingerprint_similarity(d0, d1, 0.5)
+    # Completely different datasets
+    assert_fingerprint_similarity(d0, d3, 0.0)
+    # Train + test datasets vs. test dataset only
+    # Ratio if 60000+10000 = 70000 vs. 10000 data points
+    # Expected similarity is 1/7 ~= 0.1428
+    assert_fingerprint_similarity(d2, d3, 0.1428)
+
+
+def test_serialization():
+    # Get the first element from the dataset
+    sample = next(iter(test_data))
+    img: np.ndarray = sample["image"].numpy()
+    label: int = int(sample["label"].numpy())
+    serialized = TensorFlowDataset.serialize_data_point({"image": img, "label": label})
+    deserialized = TensorFlowDataset.deserialize_data_point(serialized)
+
+    assert label == deserialized["label"]
+    assert np.array_equal(img, deserialized["image"])
diff --git a/datasig/test/test_torch_mnist.py b/datasig/test/test_torch_mnist.py
index bd2bbb4..ff6c096 100644
--- a/datasig/test/test_torch_mnist.py
+++ b/datasig/test/test_torch_mnist.py
@@ -649,7 +649,7 @@ def test_mnist_v0():
         b'W\xb6\xef\xde\xf1T2\xecp;\x86\x0eP\xd5\xafV\x87\x08\xedJ{\x17+h'
         b'\x11\x84\xe2\xf7\x00\x058\xfb\x80t\x19\x01=yA1\xd5_xh\xa5G\x96\x8d'
         b'\xea\xc6\xce\xd9j\xbb\xf5P\xa4l~2'
-)
+    )
 
 
 def test_mnist_similarity():
diff --git a/pyproject.toml b/pyproject.toml
index 4404b94..e21f6de 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,7 +1,7 @@
 [project]
 name = "datasig"
 version = "0.0.4"
-requires-python = ">= 3.8"
+requires-python = ">= 3.10"
 readme = "README.md"
 description = "Dataset fingerprinting library"
 authors = [
@@ -17,6 +17,8 @@ dependencies = [
     "numpy>=1.24.4",
     "pillow>=10.4.0",
     "torch>=2.5.1",
+    "tensorflow>=2.19.0",
+    "tensorflow-datasets>=4.9.0",
 ]
 
 [dependency-groups]
diff --git a/uv.lock b/uv.lock
index 540e69b..ef582cb 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,12 +1,60 @@
 version = 1
-revision = 3
-requires-python = ">=3.8"
+requires-python = ">=3.10"
 resolution-markers = [
-    "python_full_version >= '3.12'",
+    "python_full_version >= '3.13'",
+    "python_full_version == '3.12.*'",
     "python_full_version == '3.11.*'",
-    "python_full_version == '3.10.*'",
-    "python_full_version == '3.9.*'",
-    "python_full_version < '3.9'",
+    "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "absl-py"
+version = "2.3.1"
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+]
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+[[package]]
+name = "array-record"
+version = "0.8.1"
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+dependencies = [
+    { name = "absl-py" },
+    { name = "etils", extra = ["epath"] },
+]
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+    { url = "https://files.pythonhosted.org/packages/8b/0f/b7aa8c9a989d09c9d48f4a674546d71aa65f525134c357b03ab4cbf5a59e/array_record-0.8.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:005fb6f4f4e833b2de1c4e51c620ad6a30a893b46dc168039d9bb31b71466070", size = 2465197 },
+    { url = "https://files.pythonhosted.org/packages/8a/18/260f8f0b024653b4f93848bf0c6093f53d73c330357b1855cf8817e90036/array_record-0.8.1-cp313-cp313-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:86478265c96df1ec5cf2e124aa4a416e033eef9da9f72b05a776cee9871fb00a", size = 2407691 },
+    { url = "https://files.pythonhosted.org/packages/17/ab/3eaebf9a2b4b3b4f06c83c854ebd5dfd77934d5a84ee94df80f159bb1c4a/array_record-0.8.1-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:37bb802e1fdc98edb60a859552c7e7d494e141cf390bfb7350acfd63467daeae", size = 2465489 },
+]
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+[[package]]
+name = "astunparse"
+version = "1.6.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+    { name = "wheel" },
+]
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+name = "attrs"
+version = "25.4.0"
+source = { registry = "https://pypi.org/simple" }
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 [[package]]
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 version = "24.8.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
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     { name = "pillow", specifier = ">=10.4.0" },
+    { name = "tensorflow", specifier = ">=2.19.0" },
+    { name = "tensorflow-datasets", specifier = ">=4.9.0" },
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@@ -1570,11 +1801,11 @@ name = "platformdirs"
 version = "4.3.6"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.9'",
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