diff --git a/recognition/arcface_torch/torch2onnx.py b/recognition/arcface_torch/torch2onnx.py
index 1b7c322..f0bcfac 100644
--- a/recognition/arcface_torch/torch2onnx.py
+++ b/recognition/arcface_torch/torch2onnx.py
@@ -19,3 +19,36 @@ def convert_onnx(net, path_module, output, opset=11):
     graph = model.graph
     graph.input[0].type.tensor_type.shape.dim[0].dim_param = 'None'
     onnx.save(model, output)
+
+    
+if __name__ == '__main__':
+    import os
+    import argparse
+    from backbones import get_model
+
+    parser = argparse.ArgumentParser(description='ArcFace PyTorch to onnx')
+    parser.add_argument('input', type=str, help='input backbone.pth file or path')
+    parser.add_argument('--output', type=str, default=None, help='output onnx path')
+    parser.add_argument('--network', type=str, default=None, help='backbone network')
+    args = parser.parse_args()
+    input_file = args.input
+    if os.path.isdir(input_file):
+        input_file = os.path.join(input_file, "backbone.pth")
+    assert os.path.exists(input_file)
+    model_name = os.path.basename(os.path.dirname(input_file)).lower()
+    params = model_name.split("_")
+    if len(params) >= 3 and params[1] in ('arcface', 'cosface'):
+        if args.network is None:
+            args.network = params[2]
+    assert args.network is not None
+    print(args)
+    backbone_onnx = get_model(args.network, dropout=0)
+
+    output_path = args.output
+    if output_path is None:
+        output_path = os.path.join(os.path.dirname(__file__), 'onnx')
+    if not os.path.exists(output_path):
+        os.makedirs(output_path)
+    assert os.path.isdir(output_path)
+    output_file = os.path.join(output_path, "%s.onnx" % model_name)
+    convert_onnx(backbone_onnx, input_file, output_file)