diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..7dba25f
--- /dev/null
+++ b/requirements.txt
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+numpy
+timm>=0.5.4
diff --git a/timm_image.py b/timm_image.py
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--- /dev/null
+++ b/timm_image.py
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+import numpy
+import torch
+from typing import NamedTuple
+
+from towhee.operator.base import NNOperator
+from towhee.utils.pil_utils import to_pil
+from towhee.types.image import Image as towheeImage
+
+from torch import nn
+
+from timm.data.transforms_factory import create_transform
+from timm.data import resolve_data_config
+from timm.models.factory import create_model
+
+import warnings
+warnings.filterwarnings('ignore')
+
+class TimmImage(NNOperator):
+    """
+    Pytorch image embedding operator that uses the Pytorch Image Model (timm) collection.
+    Args:
+        model_name (`str`):
+            Which model to use for the embeddings.
+    """
+    def __init__(self, model_name: str, num_classes: int = 1000) -> None:
+        super().__init__()
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.model = create_model(model_name, pretrained=True, num_classes=num_classes)
+        self.model.to(self.device)
+        self.model.eval()
+        config = resolve_data_config({}, model=self.model)
+        self.tfms = create_transform(**config)
+
+    def __call__(self, image: 'towheeImage') -> NamedTuple('Outputs', [('vec', numpy.ndarray)]):
+        img = self.tfms(to_pil(image)).unsqueeze(0)
+        img = img.to(self.device)
+        features = self.model.forward_features(img)
+        if features.dim() == 4:
+            global_pool = nn.AdaptiveAvgPool2d(1)
+            features = global_pool(features)
+
+        features = features.to('cpu')
+        feature_vector = features.flatten().detach().numpy()
+        Outputs = NamedTuple('Outputs', [('vec', numpy.ndarray)])
+        return Outputs(feature_vector)